[0.9.4-rc1] Default WAL settings causing write timeouts

Some flushes from a low-traffic database:
[wal] 2015/09/14 08:50:09 Flush due to idle. Flushing 14 series with 27 points and 459 bytes from partition 1
[wal] 2015/09/14 08:50:09 write to index of partition 1 took 9.622795ms
[wal] 2015/09/14 08:50:19 Flush due to idle. Flushing 1 series with 1 points and 17 bytes from partition 1
[wal] 2015/09/14 08:50:19 write to index of partition 1 took 5.204475ms
[wal] 2015/09/14 08:50:34 Flush due to idle. Flushing 15 series with 15 points and 255 bytes from partition 1
[wal] 2015/09/14 08:50:34 write to index of partition 1 took 3.627889ms
[wal] 2015/09/14 08:50:44 Flush due to idle. Flushing 19 series with 19 points and 323 bytes from partition 1
[wal] 2015/09/14 08:50:44 write to index of partition 1 took 6.762247ms

After 24 minutes of uptime, the WAL from the big database is being flushed:
[wal] 2015/09/14 08:50:53 Flush due to memory. Flushing 327206 series with 1234168 points and 20980856 bytes from partition 1
[wal] 2015/09/14 08:50:54 Flush due to idle. Flushing 31 series with 34 points and 578 bytes from partition 1
[wal] 2015/09/14 08:50:54 write to index of partition 1 took 3.052655ms

1234168 points in 24 minutes equal 900 points per second - which is not really a lot?
Now, timeouts start to appear:
[udp] 2015/09/14 08:51:02 failed to write point batch to database "xxx_prod": timeout
[wal] 2015/09/14 08:51:05 Flush due to idle. Flushing 5 series with 16 points and 272 bytes from partition 1
[wal] 2015/09/14 08:51:05 write to index of partition 1 took 2.245901ms
[udp] 2015/09/14 08:51:07 failed to write point batch to database "xxx_prod": timeout
[udp] 2015/09/14 08:51:12 failed to write point batch to database "xxx_prod": timeout
[udp] 2015/09/14 08:51:17 failed to write point batch to database "xxx_prod": timeout
[udp] 2015/09/14 08:51:22 failed to write point batch to database "xxx_prod": timeout
[udp] 2015/09/14 08:51:27 failed to write point batch to database "xxx_prod": timeout
[udp] 2015/09/14 08:51:32 failed to write point batch to database "xxx_prod": timeout
[udp] 2015/09/14 08:51:37 failed to write point batch to database "xxx_prod": timeout

That continues until the flush operation is complete:
[wal] 2015/09/14 08:53:23 write to index of partition 1 took 2m30.416279098s

For now, I reduced wal-partition-size-threshold to 600000. This seems to be a good number for my setup. Flushes now complete in 2-5 seconds.
[wal] 2015/09/14 09:26:46 Flush due to memory. Flushing 24499 series with 38244 points and 650148 bytes from partition 1
[wal] 2015/09/14 09:27:21 Flush due to memory. Flushing 27640 series with 44482 points and 756194 bytes from partition 1
[wal] 2015/09/14 09:27:51 Flush due to memory. Flushing 24657 series with 39907 points and 678419 bytes from partition 1
[wal] 2015/09/14 09:28:21 Flush due to memory. Flushing 24104 series with 38973 points and 662541 bytes from partition 1
[wal] 2015/09/14 09:28:46 Flush due to memory. Flushing 22132 series with 37176 points and 631992 bytes from partition 1

However, I would like to know whether the flush is IO or CPU bound. Looking at the process statistics, neither seems to be particularly saturated during the flush. Furthermore, InfluxDB does not shut down cleanly while a flush is happening.

After running okay for a while, writes started to fail again also with the updated WAL settings:
[udp] 2015/09/14 13:10:22 failed to write point batch to database "xxx_prod": timeout
...
[udp] 2015/09/14 13:16:51 failed to write point batch to database "xxx_prod": timeout
[udp] 2015/09/14 13:18:32 failed to write point batch to database "xxx_prod": timeout
[udp] 2015/09/14 13:20:29 failed to write point batch to database "xxx_prod": timeout
[udp] 2015/09/14 13:20:37 failed to write point batch to database "xxx_prod": timeout
[udp] 2015/09/14 13:20:45 failed to write point batch to database "xxx_prod": timeout
[write] 2015/09/14 13:30:48 write failed for shard 2 on node 1: engine: write points: write throughput too high. backoff and retry
[udp] 2015/09/14 13:30:48 failed to write point batch to database "xxx_prod": write failed: engine: write points: write throughput too high. backoff and retry
[write] 2015/09/14 13:30:53 write failed for shard 2 on node 1: engine: write points: write throughput too high. backoff and retry
[udp] 2015/09/14 13:30:53 failed to write point batch to database "xxx_prod": write failed: engine: write points: write throughput too high. backoff and retry
[write] 2015/09/14 13:30:58 write failed for shard 2 on node 1: engine: write points: write throughput too high. backoff and retry
[udp] 2015/09/14 13:30:58 failed to write point batch to database "xxx_prod": write failed: engine: write points: write throughput too high. backoff and retry
[write] 2015/09/14 13:31:04 write failed for shard 2 on node 1: engine: write points: write throughput too high. backoff and retry
[udp] 2015/09/14 13:31:04 failed to write point batch to database "xxx_prod": write failed: engine: write points: write throughput too high. backoff and retry
[write] 2015/09/14 13:31:08 write failed for shard 2 on node 1: engine: write points: write throughput too high. backoff and retry
[udp] 2015/09/14 13:31:08 failed to write point batch to database "xxx_prod": write failed: engine: write points: write throughput too high. backoff and retry
[write] 2015/09/14 13:31:13 write failed for shard 2 on node 1: engine: write points: write throughput too high. backoff and retry
[udp] 2015/09/14 13:31:13 failed to write point batch to database "xxx_prod": write failed: engine: write points: write throughput too high. backoff and retry
[write] 2015/09/14 13:31:18 write failed for shard 2 on node 1: engine: write points: write throughput too high. backoff and retry
[udp] 2015/09/14 13:31:18 failed to write point batch to database "xxx_prod": write failed: engine: write points: write throughput too high. backoff and retry
[write] 2015/09/14 13:31:23 write failed for shard 2 on node 1: engine: write points: write throughput too high. backoff and retry
[udp] 2015/09/14 13:31:23 failed to write point batch to database "xxx_prod": write failed: engine: write points: write throughput too high. backoff and retry
[write] 2015/09/14 13:31:28 write failed for shard 2 on node 1: engine: write points: write throughput too high. backoff and retry
[udp] 2015/09/14 13:31:28 failed to write point batch to database "xxx_prod": write failed: engine: write points: write throughput too high. backoff and retry
[write] 2015/09/14 13:31:33 write failed for shard 2 on node 1: engine: write points: write throughput too high. backoff and retry
[udp] 2015/09/14 13:31:33 failed to write point batch to database "xxx_prod": write failed: engine: write points: write throughput too high. backoff and retry
[write] 2015/09/14 13:31:38 write failed for shard 2 on node 1: engine: write points: write throughput too high. backoff and retry
[udp] 2015/09/14 13:31:38 failed to write point batch to database "xxx_prod": write failed: engine: write points: write throughput too high. backoff and retry
[write] 2015/09/14 13:31:43 write failed for shard 2 on node 1: engine: write points: write throughput too high. backoff and retry
[udp] 2015/09/14 13:31:43 failed to write point batch to database "xxx_prod": write failed: engine: write points: write throughput too high. backoff and retry
[write] 2015/09/14 13:31:48 write failed for shard 2 on node 1: engine: write points: write throughput too high. backoff and retry
[udp] 2015/09/14 13:31:48 failed to write point batch to database "xxx_prod": write failed: engine: write points: write throughput too high. backoff and retry
[write] 2015/09/14 13:31:53 write failed for shard 2 on node 1: engine: write points: write throughput too high. backoff and retry
[udp] 2015/09/14 13:31:53 failed to write point batch to database "xxx_prod": write failed: engine: write points: write throughput too high. backoff and retry
[write] 2015/09/14 13:31:58 write failed for shard 2 on node 1: engine: write points: write throughput too high. backoff and retry
[udp] 2015/09/14 13:31:58 failed to write point batch to database "xxx_prod": write failed: engine: write points: write throughput too high. backoff and retry
[write] 2015/09/14 13:32:03 write failed for shard 2 on node 1: engine: write points: write throughput too high. backoff and retry
[udp] 2015/09/14 13:32:03 failed to write point batch to database "xxx_prod": write failed: engine: write points: write throughput too high. backoff and retry
[write] 2015/09/14 13:32:08 write failed for shard 2 on node 1: engine: write points: write throughput too high. backoff and retry
[udp] 2015/09/14 13:32:08 failed to write point batch to database "xxx_prod": write failed: engine: write points: write throughput too high. backoff and retry
[write] 2015/09/14 13:32:13 write failed for shard 2 on node 1: engine: write points: write throughput too high. backoff and retry
[udp] 2015/09/14 13:32:13 failed to write point batch to database "xxx_prod": write failed: engine: write points: write throughput too high. backoff and retry
[udp] 2015/09/14 13:32:26 failed to write point batch to database "xxx_prod": timeout
[udp] 2015/09/14 13:32:34 failed to write point batch to database "xxx_prod": timeout
...
[udp] 2015/09/14 13:38:55 failed to write point batch to database "xxx_prod": timeout
[udp] 2015/09/14 13:39:03 failed to write point batch to database "xxx_prod": timeout
[udp] 2015/09/14 13:39:11 failed to write point batch to database "xxx_prod": timeout

At this point, influxdb was killed due to OOM. I started it back up a few minutes later.
[wal] 2015/09/14 13:53:39 reading WAL files to flush to index
[wal] 2015/09/14 13:53:40 writing 46817 series from WAL file /var/opt/influxdb/wal/xxx_prod/default/2/01.000480.wal to index
...
[wal] 2015/09/14 14:15:14 writing 0 series from WAL file /var/opt/influxdb/wal/xxx_prod/default/2/01.000482.wal to index

After writing from the WAL to the index (which took almost 20 minutes!) it ran okay for about a minute, then crashed with a stack trace due to not being able to allocate memory. The full log file is here: https://gist.github.com/KiNgMaR/b7bb1969acd21225177e
I will of course patiently wait for new storage engine, but am a little surprised that 0.9.x has trouble handling this lite workload where 0.8 did not :)

@KiNgMaR,
We're trying to reproduce your situation. Would you mind sending along your config file and giving some details about your RAM, CPU, and IOPS? Also - how many points per second were you writing and were you running any queries at the time? Thanks!

Hey @rkuchan, thanks for looking into it! I can send the full log file (including [query] entries) via mail (it may contain some "sensitive" data). Which address can I send it to?
Points per second = about 900
System specs:

VMware 6 virtual machine
8 cores (5399.99 bogomips each)
16 GB RAM
CentOS 6.6
Kernel 2.6.32-504.30.3.el6.centos.plus.x86_64
nothing else is running on the machine
the /var/opt/influxdb/data/ directory is mounted via NFSv3 (10 GBit Ethernet, 155 MB/s write throughput according to dd, latency should also be fairly good, https://gist.github.com/KiNgMaR/7440e0369b01e291fed0)

Config file: https://gist.github.com/KiNgMaR/b1232f45a9eb12fa2f8f
Thank you!

No problem! You can upload your stuff here: https://www.cloudwok.com/cu/gh4096.

Logs uploaded. Thanks!

We see similar issue with stable 0.9.4 release. The write request time out number was more or less a constant for almost one day, and today the time out number begin linearly increasing.
The log shows points write time out after wal flush:
[wal] 2015/09/18 02:54:05 Flush due to idle. Flushing 23 series with 27 points and 1156 bytes from partition 1
[wal] 2015/09/18 02:54:07 Flush due to idle. Flushing 24 series with 29 points and 1236 bytes from partition 1
[wal] 2015/09/18 02:54:07 Flush due to idle. Flushing 10 series with 10 points and 945 bytes from partition 1
[wal] 2015/09/18 02:54:07 Flush due to idle. Flushing 23 series with 29 points and 1234 bytes from partition 1
[wal] 2015/09/18 02:54:07 Flush due to idle. Flushing 135 series with 158 points and 5549 bytes from partition 1
[wal] 2015/09/18 02:54:07 Flush due to idle. Flushing 70 series with 70 points and 4322 bytes from partition 1
[wal] 2015/09/18 02:54:09 Flush due to idle. Flushing 22 series with 22 points and 1722 bytes from partition 1
[wal] 2015/09/18 02:54:10 Flush due to idle. Flushing 91 series with 199 points and 14040 bytes from partition 1
[wal] 2015/09/18 02:54:10 Flush due to idle. Flushing 35 series with 78 points and 3778 bytes from partition 1
[wal] 2015/09/18 02:54:10 Flush due to idle. Flushing 50 series with 8433 points and 445509 bytes from partition 1
[wal] 2015/09/18 02:54:10 Flush due to idle. Flushing 170 series with 18159 points and 967350 bytes from partition 1
[wal] 2015/09/18 02:54:10 Flush due to idle. Flushing 3586 series with 26962 points and 3657323 bytes from partition 1
[http] 2015/09/18 02:54:10 <IP_ADDRESS> - root [18/Sep/2015:02:54:05 +0000] POST /write?consistency=one&db=tenant_0a9faca93d4742f7aeb94c459fcbdf15&p=%5BREDACTED%5D&precision=n&rp=default&u=root HTTP/1.1 500 32 - okhttp/2.4.0 86738c95-5db0-11e5-b056-000000000000 5.000761073s
[http] 2015/09/18 02:54:10 <IP_ADDRESS> - root [18/Sep/2015:02:54:05 +0000] POST /write?consistency=one&db=tenant_0a9faca93d4742f7aeb94c459fcbdf15&p=%5BREDACTED%5D&precision=n&rp=default&u=root HTTP/1.1 500 32 - okhttp/2.4.0 867393a5-5db0-11e5-b057-000000000000 5.000729647s
[http] 2015/09/18 02:54:10 <IP_ADDRESS> - root [18/Sep/2015:02:54:05 +0000] POST /write?consistency=one&db=tenant_0a9faca93d4742f7aeb94c459fcbdf15&p=%5BREDACTED%5D&precision=n&rp=default&u=root HTTP/1.1 500 32 - okhttp/2.4.0 86738bc2-5db0-11e5-b055-000000000000 5.001165028s
[http] 2015/09/18 02:54:10 <IP_ADDRESS> - root [18/Sep/2015:02:54:05 +0000] POST /write?consistency=one&db=tenant_0a9faca93d4742f7aeb94c459fcbdf15&p=%5BREDACTED%5D&precision=n&rp=default&u=root HTTP/1.1 500 32 - okhttp/2.4.0 8673bd16-5db0-11e5-b058-000000000000 5.000585795s
[http] 2015/09/18 02:54:10 <IP_ADDRESS> - root [18/Sep/2015:02:54:05 +0000] POST /write?consistency=one&db=tenant_0a9faca93d4742f7aeb94c459fcbdf15&p=%5BREDACTED%5D&precision=n&rp=default&u=root HTTP/1.1 500 32 - okhttp/2.4.0 86741da2-5db0-11e5-b059-000000000000 5.000806163s
[http] 2015/09/18 02:54:10 <IP_ADDRESS> - root [18/Sep/2015:02:54:05 +0000] POST /write?consistency=one&db=tenant_0a9faca93d4742f7aeb94c459fcbdf15&p=%5BREDACTED%5D&precision=n&rp=default&u=root HTTP/1.1 500 32 - okhttp/2.4.0 8674882e-5db0-11e5-b05b-000000000000 5.000872165s
[http] 2015/09/18 02:54:10 <IP_ADDRESS> - root [18/Sep/2015:02:54:05 +0000] POST /write?consistency=one&db=tenant_0a9faca93d4742f7aeb94c459fcbdf15&p=%5BREDACTED%5D&precision=n&rp=default&u=root HTTP/1.1 500 32 - okhttp/2.4.0 86748739-5db0-11e5-b05a-000000000000 5.001003338s
[http] 2015/09/18 02:54:10 <IP_ADDRESS> - root [18/Sep/2015:02:54:05 +0000] POST /write?consistency=one&db=tenant_0a9faca93d4742f7aeb94c459fcbdf15&p=%5BREDACTED%5D&precision=n&rp=default&u=root HTTP/1.1 500 32 - okhttp/2.4.0 86748f49-5db0-11e5-b05c-000000000000 5.000976884s
[http] 2015/09/18 02:54:10 <IP_ADDRESS> - root [18/Sep/2015:02:54:05 +0000] POST /write?consistency=one&db=tenant_0a9faca93d4742f7aeb94c459fcbdf15&p=%5BREDACTED%5D&precision=n&rp=default&u=root HTTP/1.1 500 32 - okhttp/2.4.0 8675a644-5db0-11e5-b05d-000000000000 5.000734955s
[http] 2015/09/18 02:54:10 <IP_ADDRESS> - root [18/Sep/2015:02:54:05 +0000] POST /write?consistency=one&db=tenant_0a9faca93d4742f7aeb94c459fcbdf15&p=%5BREDACTED%5D&precision=n&rp=default&u=root HTTP/1.1 500 32 - okhttp/2.4.0 8675da27-5db0-11e5-b05e-000000000000 5.000780546s
[http] 2015/09/18 02:54:10 <IP_ADDRESS> - root [18/Sep/2015:02:54:05 +0000] POST /write?consistency=one&db=tenant_0a9faca93d4742f7aeb94c459fcbdf15&p=%5BREDACTED%5D&precision=n&rp=default&u=root HTTP/1.1 500 32 - okhttp/2.4.0 8676aa5e-5db0-11e5-b05f-000000000000 5.00076614s
[http] 2015/09/18 02:54:10 <IP_ADDRESS> - root [18/Sep/2015:02:54:05 +0000] POST /write?consistency=one&db=tenant_0a9faca93d4742f7aeb94c459fcbdf15&p=%5BREDACTED%5D&precision=n&rp=default&u=root HTTP/1.1 500 32 - okhttp/2.4.0 8676ef3c-5db0-11e5-b060-000000000000 5.000874004s
[http] 2015/09/18 02:54:10 <IP_ADDRESS> - root [18/Sep/2015:02:54:05 +0000] POST /write?consistency=one&db=tenant_0a9faca93d4742f7aeb94c459fcbdf15&p=%5BREDACTED%5D&precision=n&rp=default&u=root HTTP/1.1 500 32 - okhttp/2.4.0 86774fc2-5db0-11e5-b061-000000000000 5.000810524s
[http] 2015/09/18 02:54:10 <IP_ADDRESS> - root [18/Sep/2015:02:54:05 +0000] POST /write?consistency=one&db=tenant_0a9faca93d4742f7aeb94c459fcbdf15&p=%5BREDACTED%5D&precision=n&rp=default&u=root HTTP/1.1 500 32 - okhttp/2.4.0 86777019-5db0-11e5-b062-000000000000 5.001051703s
[http] 2015/09/18 02:54:10 <IP_ADDRESS> - root [18/Sep/2015:02:54:05 +0000] POST /write?consistency=one&db=tenant_0a9faca93d4742f7aeb94c459fcbdf15&p=%5BREDACTED%5D&precision=n&rp=default&u=root HTTP/1.1 500 32 - okhttp/2.4.0 8677839a-5db0-11e5-b063-000000000000 5.001026917s
[http] 2015/09/18 02:54:11 <IP_ADDRESS> - root [18/Sep/2015:02:54:06 +0000] POST /write?consistency=one&db=tenant_c723e4f7bf364baeb1f6fbb2b41011f6&p=%5BREDACTED%5D&precision=n&rp=default&u=root HTTP/1.1 500 32 - okhttp/2.4.0 870740c1-5db0-11e5-b067-000000000000 5.001594968s
[http] 2015/09/18 02:54:11 <IP_ADDRESS> - root [18/Sep/2015:02:54:06 +0000] POST /write?consistency=one&db=tenant_c723e4f7bf364baeb1f6fbb2b41011f6&p=%5BREDACTED%5D&precision=n&rp=default&u=root HTTP/1.1 500 32 - okhttp/2.4.0 87073e19-5db0-11e5-b066-000000000000 5.001673715s
[http] 2015/09/18 02:54:11 <IP_ADDRESS> - root [18/Sep/2015:02:54:06 +0000] POST /write?consistency=one&db=tenant_c723e4f7bf364baeb1f6fbb2b41011f6&p=%5BREDACTED%5D&precision=n&rp=default&u=root HTTP/1.1 500 32 - okhttp/2.4.0 87074bae-5db0-11e5-b068-000000000000 5.001586573s
[http] 2015/09/18 02:54:11 <IP_ADDRESS> - root [18/Sep/2015:02:54:06 +0000] POST /write?consistency=one&db=tenant_c723e4f7bf364baeb1f6fbb2b41011f6&p=%5BREDACTED%5D&precision=n&rp=default&u=root HTTP/1.1 500 32 - okhttp/2.4.0 87073256-5db0-11e5-b064-000000000000 5.002341997s
[http] 2015/09/18 02:54:11 <IP_ADDRESS> - root [18/Sep/2015:02:54:06 +0000] POST /write?consistency=one&db=tenant_c723e4f7bf364baeb1f6fbb2b41011f6&p=%5BREDACTED%5D&precision=n&rp=default&u=root HTTP/1.1 500 32 - okhttp/2.4.0 87073cb6-5db0-11e5-b065-000000000000 5.002248086s
here is an image of the stats we are collecting from _internal database

Hi,
I started exploring InfluxDB and ran into same issue with version <IP_ADDRESS>.
Flushing takes lot of time (in my case it took close to 19 mins for 620K series). Please refer below logs
[wal] 2015/10/01 12:48:36 Flush due to memory. Flushing 620000 series with 620000 points and 21700000 bytes from partition 1
[wal] 2015/10/01 13:07:20 write to index of partition 1 took 18m44.1748336s
[wal] 2015/10/01 13:08:04 Metadata flush took 8m58.9704412s
The server has 4 cores and 16 GB RAM.
Here is the configuration
[data]
engine = "bz1"
max-wal-size = 104857600
wal-flush-interval = "2m0s"
wal-partition-flush-delay = "2s"
wal-logging-enabled = true
wal-ready-series-size = 30720
wal-compaction-threshold = 0.5
wal-max-series-size = 1048576
wal-flush-cold-interval = "10m"
wal-partition-size-threshold = 20971520
query-log-enabled = true

Update from 0.9.5-nightly-64cef0f: no more timeouts, overall great performance, but still leaking a lot of memory and OOM'ing after a few hours.

@KiNgMaR -- how do you know it is a leak? Are you tracking RSS for the influxd process?

And @KiNgMaR -- did you change your engine? If you're still on bz1 you are running the same engine as ever. Is it your intention to try out the new tsm1 engine?

Hi @otoolep, yes tsm1 is used exclusively.
It looks like the growing memory is related to the index flush from the WAL. I made a simple log file that tracks memory usage. It can be correlated against the flush to index took entries in the influxdb.log file very easily. Here's the gist:
# grep -P 'flush to index took [\d.]+s' /var/log/influxdb/influxd.log
[tsm1wal] 2015/10/09 07:59:18 /var/opt/influxdb/data/xxx_prod/default/2 flush to index took 2.726795084s
[tsm1wal] 2015/10/09 08:03:45 /var/opt/influxdb/data/xxx_prod/default/2 flush to index took 6.140263472s
[tsm1wal] 2015/10/09 08:08:58 /var/opt/influxdb/data/xxx_prod/default/2 flush to index took 8.834235482s
[tsm1wal] 2015/10/09 08:13:35 /var/opt/influxdb/data/xxx_prod/default/2 flush to index took 11.25431678s
[tsm1wal] 2015/10/09 08:18:08 /var/opt/influxdb/data/xxx_prod/default/2 flush to index took 13.181981543s
[tsm1wal] 2015/10/09 08:22:47 /var/opt/influxdb/data/xxx_prod/default/2 flush to index took 14.84928816s
[tsm1wal] 2015/10/09 08:28:03 /var/opt/influxdb/data/xxx_prod/default/2 flush to index took 17.317131844s
[tsm1wal] 2015/10/09 08:33:03 /var/opt/influxdb/data/xxx_prod/default/2 flush to index took 19.522796355s
[tsm1wal] 2015/10/09 08:38:12 /var/opt/influxdb/data/xxx_prod/default/2 flush to index took 21.060538741s
[tsm1wal] 2015/10/09 08:45:13 /var/opt/influxdb/data/xxx_prod/default/2 flush to index took 23.749604097s
[tsm1wal] 2015/10/09 08:51:54 /var/opt/influxdb/data/xxx_prod/default/2 flush to index took 25.542913184s
[tsm1wal] 2015/10/09 08:58:48 /var/opt/influxdb/data/xxx_prod/default/2 flush to index took 27.776700584s
[tsm1wal] 2015/10/09 09:04:31 /var/opt/influxdb/data/xxx_prod/default/2 flush to index took 29.923418532s
[tsm1wal] 2015/10/09 09:09:55 /var/opt/influxdb/data/xxx_prod/default/2 flush to index took 32.794877166s
[tsm1wal] 2015/10/09 09:14:36 /var/opt/influxdb/data/xxx_prod/default/2 flush to index took 33.967796218s
[tsm1wal] 2015/10/09 09:19:33 /var/opt/influxdb/data/xxx_prod/default/2 flush to index took 36.403752239s
[tsm1wal] 2015/10/09 09:24:47 /var/opt/influxdb/data/xxx_prod/default/2 flush to index took 39.106057756s
[tsm1wal] 2015/10/09 09:29:59 /var/opt/influxdb/data/xxx_prod/default/2 flush to index took 41.738886357s
[tsm1wal] 2015/10/09 09:34:42 /var/opt/influxdb/data/xxx_prod/default/2 flush to index took 43.347510438s

TIME     RSS
07:59:06 660272
07:59:16 753816
07:59:26 791616
07:59:36 791636
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08:03:07 915040
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08:18:08 2160248
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08:22:09 2168604
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08:28:09 2922080
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08:33:00 2943488
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08:38:00 3148904
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08:51:01 2520904
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09:04:03 4042496
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09:24:35 5838672
09:24:45 6233944
09:24:55 6291920
09:29:06 6294416
09:29:16 6294416
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09:29:36 6295068
09:29:46 6303940
09:29:56 6303956
09:34:06 6313620
09:34:17 6313620
09:34:27 6314056
09:34:37 6769228
09:34:47 6780404
09:34:57 6780412

Let me know if you need all the details, and where I can upload them. My current configuration is this: https://gist.github.com/KiNgMaR/2f0ba7ca6af1f83f39bc

I think this issue has become mixed with reports for multiple engines.
@KiNgMaR -- can you open a new ticket for issues specifically related to the tsm1 engine? Tickets filed against older engines (like this one) are unlikely to be fixed.

Okay, fair enough.


missing TF variable for  app connector group custom version profile

Hi Zscaler Team,
We are trying to upgrade our connectors to a custom version. Looking at the TF variable there is no option for custom. Kindly advise.

@ashim-shrestha2020 please provide an example and the name of the resource you're trying to use.
Thanks

Hi Will,
Apologies for the delay resource we are using is zpa_app_connector_group. We have a custom version we are trying to upload.
TF registry only has version_profile_id as the following:
0 = Default
1 = Previous Default
2 = New Release
currently we have commented version_profile_id and did a manual push.


[Feature Request] Insert music ID

Hi bro,
Can you figure out how to upload video with music ID so video's sound will be not Original Sound but it shows a Title of music from the ID?
Thanks

sorry, the tiktok website does not provide this feature.
this script is just grab the website api.


Search Control cmd+click opens new tab

Fixes #2615. Would appreciate a test on windows as well 🙏

coverage: 94.33% (+0.001%) from 94.329% when pulling bc3db89c876502a7cdc2d65e47b7eee58c4b3aaf on rahul-react-search-cmd-click into 7d31f8c5aa016367329e6f5493d9b07004e13b40 on main.


[Bug Report] el-upload adds accept attributes and drag doesn't work

Element UI version
2.6.2
OS/Browsers version
win7/chrome
Vue version
2.6.10
Reproduction Link
https://jsfiddle.net/wnga3qhy/
Steps to reproduce

将文件拖到此处，或点击上传
只能上传excel文件，且不超过500kb

What is Expected?
有accept属性时仍支持drag
What is actually happening?
drag无效

me too

I have the same problem


ONIX Version support (3.0 vs 2.1)

Is there any way to distinguish what version of ONIX is being parsed?

This is addressed in PR #5


brook: 20221010 -> 20230101

Automatic update generated by nixpkgs-update tools. This update was made based on information from https://repology.org/metapackage/brook/versions.
meta.description for brook is: "A cross-platform Proxy/VPN software"
meta.homepage for brook is: "https://github.com/txthinking/brook"
meta.changelog for brook is: ""
Updates performed

Golang update

To inspect upstream changes

Release on GitHub

Compare changes on GitHub

Impact

Checks done (click to expand)

built on NixOS
The tests defined in passthru.tests, if any, passed
found 20221212 with grep in /nix/store/m2apl4427d0zjkgbmdg23wqpm6gn26vc-brook-20221212
found 20221212 in filename of file in /nix/store/m2apl4427d0zjkgbmdg23wqpm6gn26vc-brook-20221212

Rebuild report (if merged into master) (click to expand)

1 total rebuild path(s)

1 package rebuild(s)

First fifty rebuilds by attrpath
brook

Instructions to test this update (click to expand)

Either download from Cachix:
nix-store -r /nix/store/m2apl4427d0zjkgbmdg23wqpm6gn26vc-brook-20221212 \
  --option binary-caches 'https://cache.nixos.org/ https://nix-community.cachix.org/' \
  --option trusted-public-keys '
  nix-community.cachix.org-1:mB9FSh9qf2dCimDSUo8Zy7bkq5CX+/rkCWyvRCYg3Fs=
  cache.nixos.org-1:6NCHdD59X431o0gWypbMrAURkbJ16ZPMQFGspcDShjY=
  '

(The Cachix cache is only trusted for this store-path realization.)
For the Cachix download to work, your user must be in the trusted-users list or you can use sudo since root is effectively trusted.
Or, build yourself:
nix-build -A brook https://github.com/r-ryantm/nixpkgs/archive/9e81bf20fbb90b56631780f446073cdbaa0e95e2.tar.gz

After you've downloaded or built it, look at the files and if there are any, run the binaries:
ls -la /nix/store/m2apl4427d0zjkgbmdg23wqpm6gn26vc-brook-20221212
ls -la /nix/store/m2apl4427d0zjkgbmdg23wqpm6gn26vc-brook-20221212/bin

Pre-merge build results
We have automatically built all packages that will get rebuilt due to
this change.
This gives evidence on whether the upgrade will break dependent packages.
Note sometimes packages show up as failed to build independent of the
change, simply because they are already broken on the target branch.
Result of nixpkgs-review run on x86_64-linux 1

1 package built:

brook

Maintainer pings
cc @xrelkd for testing.

Automatic update generated by nixpkgs-update tools. This update was made based on information from https://github.com/txthinking/brook/releases.
meta.description for brook is: A cross-platform Proxy/VPN software
meta.homepage for brook is: https://github.com/txthinking/brook
Updates performed

Golang update

To inspect upstream changes

Release on GitHub

Compare changes on GitHub

Impact

Checks done (click to expand)

built on NixOS
The tests defined in passthru.tests, if any, passed
found 20230101 with grep in /nix/store/vc9dqcnxzk3z9d9hh823mnqnjswpjb1s-brook-20230101
found 20230101 in filename of file in /nix/store/vc9dqcnxzk3z9d9hh823mnqnjswpjb1s-brook-20230101

Rebuild report (if merged into master) (click to expand)

1 total rebuild path(s)

1 package rebuild(s)

First fifty rebuilds by attrpath
brook

Instructions to test this update (click to expand)

Either download from Cachix:
nix-store -r /nix/store/vc9dqcnxzk3z9d9hh823mnqnjswpjb1s-brook-20230101 \
  --option binary-caches 'https://cache.nixos.org/ https://nix-community.cachix.org/' \
  --option trusted-public-keys '
  nix-community.cachix.org-1:mB9FSh9qf2dCimDSUo8Zy7bkq5CX+/rkCWyvRCYg3Fs=
  cache.nixos.org-1:6NCHdD59X431o0gWypbMrAURkbJ16ZPMQFGspcDShjY=
  '

(The Cachix cache is only trusted for this store-path realization.)
For the Cachix download to work, your user must be in the trusted-users list or you can use sudo since root is effectively trusted.
Or, build yourself:
nix-build -A brook https://github.com/r-ryantm/nixpkgs/archive/f32351f87b8f73227118f8d5c722a977362529dc.tar.gz

After you've downloaded or built it, look at the files and if there are any, run the binaries:
ls -la /nix/store/vc9dqcnxzk3z9d9hh823mnqnjswpjb1s-brook-20230101
ls -la /nix/store/vc9dqcnxzk3z9d9hh823mnqnjswpjb1s-brook-20230101/bin

Pre-merge build results
We have automatically built all packages that will get rebuilt due to
this change.
This gives evidence on whether the upgrade will break dependent packages.
Note sometimes packages show up as failed to build independent of the
change, simply because they are already broken on the target branch.
Result of nixpkgs-review run on x86_64-linux 1

1 package built:

brook

Maintainer pings
cc @xrelkd for testing.

Hi @marsam, I think this PR is ready to merge. Thank you.

Hi @NickCao, I think this PR is ready to merge. Thank you.


Wave

Codecov Report

Merging #39 into master will increase coverage by <.01%.
The diff coverage is 100%.

@@            Coverage Diff             @@
##           master      #39      +/-   ##
==========================================
+ Coverage   99.59%   99.59%   +<.01%
==========================================
  Files          23       23
  Lines         248      249       +1
  Branches       18       18
==========================================
+ Hits          247      248       +1
  Misses          1        1

Impacted Files
Coverage Δ

src/components/Theory/Theory.js
100% <100%> (ø)
:arrow_up:

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
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There's no create-user recipe

Checked the makefile and there is no create-user recipe plus running the command gives me this error:
make: *** No rule to make target 'create-user'.  Stop.

Yep! Also the README was a little opaque so I added more detail.


Fix scrolling in the monaco editor

For #10952
Problems with scrolling on new line (caused by scroll handler being called before the line was actually there)
Problems with scrolling when typing as opposed to just up/down arrow. (caused by scroll handler not waiting for the text to appear so scrolling to a visible line that gets destroyed)
Also fixed a bug in my style speedup code. Sometimes there are no visible lines. In that case, don't remember the default of 18PX.

Codecov Report

Merging #11089 into master will decrease coverage by 0.02%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master   #11089      +/-   ##
==========================================
- Coverage   61.42%   61.39%   -0.03%
==========================================
  Files         597      597
  Lines       32756    32756
  Branches     4631     4631
==========================================
- Hits        20121    20112       -9
- Misses      11619    11626       +7
- Partials     1016     1018       +2

Impacted Files
Coverage Δ

src/datascience-ui/react-common/arePathsSame.ts
75.00% <0.00%> (-12.50%)
:arrow_down:

src/client/common/utils/platform.ts
64.70% <0.00%> (-11.77%)
:arrow_down:

...ence/ipywidgets/localWidgetScriptSourceProvider.ts
88.13% <0.00%> (-3.39%)
:arrow_down:

src/client/linters/pydocstyle.ts
86.66% <0.00%> (-2.23%)
:arrow_down:

src/client/datascience/debugLocationTracker.ts
76.56% <0.00%> (-1.57%)
:arrow_down:

...client/datascience/kernel-launcher/kernelFinder.ts
75.70% <0.00%> (-0.94%)
:arrow_down:

src/client/common/process/proc.ts
14.49% <0.00%> (-0.73%)
:arrow_down:

...ient/datascience/interactive-ipynb/nativeEditor.ts
11.61% <0.00%> (ø)

...atascience/interactive-window/interactiveWindow.ts
15.48% <0.00%> (ø)

Continue to review full report at Codecov.

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Δ = absolute <relative> (impact), ø = not affected, ? = missing data
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Include in Docker image necessary drivers for pyodbc

Can you please make it possible to write to Azure SQL Database from this Docker image using pyodbc? It is unable to be installed because of missing drivers. Thanks!

in the conda_deoendcies.yml ; I added something like this:
dependencies:
The python interpreter version.
Currently Azure ML Workbench only supports 3.5.2 and later.

python=3.7.*

Required by azureml-defaults, installed separately through Conda to
get a prebuilt version and not require build tools for the install.
- psutil=5.6 #latest

pyodbc=4.0.28
Required packages for AzureML execution, history, and data preparation.

anaconda
pip
......

but I am getting another error now (No module named 'ruamel')

Apologies for the late reply!
Unfortunately, we won't be able to get to this right now. My recommendation is to use your own custom image: https://github.com/microsoft/MLOpsPython/blob/master/docs/custom_container.md

Apologies for the late reply!
Unfortunately, we won't be able to get to this right now. My recommendation is to use your own custom image: https://github.com/microsoft/MLOpsPython/blob/master/docs/custom_container.md

Hi @cindyweng, I was able to resolve this issue by creating a custom "run-config" environment for the ML-Ops Python,
We can install pyodbc and any other pip packages by going to ml_service/pipelines/diabetes_regression_build_train_pipeline.py
On line 37, we can replace the code with the one I provided below. This will create custom run-config and then manually we can enter the libraries to be used here.
##Defining the run configuration for the Pipeline to run
run_config = RunConfiguration()

# The compute on which the pipeline would be running
# and training the model
run_config.target = aml_compute

run_config.environment.python.conda_dependencies = \
CondaDependencies.create(
conda_packages=['pandas','scikit-learn','numpy','seaborn','matplotlib',
'pyodbc','openpyxl'],pip_packages=['azureml-sdk',
'azureml-dataset-runtime[fuse,pandas]','azureml-defaults',
'azureml-interpret','python-dotenv'],
pin_sdk_version=False)

Code Before:

After Code:

I hope this resolves this issue.
Regards,
Mudit Mathur
Data Scientist

Hi @cindyweng, I was able to resolve this issue by creating a custom "run-config" environment for the ML-Ops Python,
We can install pyodbc and any other pip packages by going to ml_service/pipelines/diabetes_regression_build_train_pipeline.py
On line 37, we can replace the code with the one I provided below. This will create custom run-config and then manually we can enter the libraries to be used here.
##Defining the run configuration for the Pipeline to run
run_config = RunConfiguration()

# The compute on which the pipeline would be running
# and training the model
run_config.target = aml_compute

run_config.environment.python.conda_dependencies = \
CondaDependencies.create(
conda_packages=['pandas','scikit-learn','numpy','seaborn','matplotlib',
'pyodbc','openpyxl'],pip_packages=['azureml-sdk',
'azureml-dataset-runtime[fuse,pandas]','azureml-defaults',
'azureml-interpret','python-dotenv'],
pin_sdk_version=False)

Code Before:

After Code:

I hope this resolves this issue.
Regards,
Mudit Mathur
Data Scientist

Hi @cindyweng, I was able to resolve this issue by creating a custom "run-config" environment for the ML-Ops Python,
We can install pyodbc and any other pip packages by going to ml_service/pipelines/diabetes_regression_build_train_pipeline.py
On line 37 and 38, we can replace the code with the one I provided below. This will create custom run-config and then manually we can enter the libraries to be used here.
##Defining the run configuration for the Pipeline to run
run_config = RunConfiguration()

# The compute on which the pipeline would be running
# and training the model
run_config.target = aml_compute

run_config.environment.python.conda_dependencies = \
CondaDependencies.create(
conda_packages=['pandas','scikit-learn','numpy','seaborn','matplotlib',
'pyodbc','openpyxl'],pip_packages=['azureml-sdk',
'azureml-dataset-runtime[fuse,pandas]','azureml-defaults',
'azureml-interpret','python-dotenv'],
pin_sdk_version=False)

Code Before:

Code After:

I hope this resolves this issue. Contributors: @muditmathur2020 and @OmkarChoulwar
Regards, Mudit Mathur Data Scientist

To summarize, adding any custom library in the environment is possible using the CondaDependencies in the azureml python.  sdk.

Hi @cindyweng, I was able to resolve this issue by creating a custom "run-config" environment for the ML-Ops Python,
We can install pyodbc and any other pip packages by going to ml_service/pipelines/diabetes_regression_build_train_pipeline.py
On line 37 and 38, we can replace the code with the one I provided below. This will create custom run-config and then manually we can enter the libraries to be used here.
##Defining the run configuration for the Pipeline to run
run_config = RunConfiguration()

# The compute on which the pipeline would be running
# and training the model
run_config.target = aml_compute

run_config.environment.python.conda_dependencies = \
CondaDependencies.create(
conda_packages=['pandas','scikit-learn','numpy','seaborn','matplotlib',
'pyodbc','openpyxl'],pip_packages=['azureml-sdk',
'azureml-dataset-runtime[fuse,pandas]','azureml-defaults',
'azureml-interpret','python-dotenv'],
pin_sdk_version=False)

Code Before:

Code After:

I hope this resolves this issue. Contributors: @muditmathur2020 and @OmkarChoulwar
Regards, Mudit Mathur Data Scientist

To summarize, adding any custom library in the environment is possible using the CondaDependencies in the azureml python.  sdk.

MOM, Can you Please, Please ask you co-workers, dog groomer, person that dose your taxes, neighbor, etc.. Looking for an Old Fix me upper VAN, preferably Cargo Van (There Cheap) but any van will do. Even a Car would work if that’s all that is available. We Can put 1000.00 down and make payments. PLEASE ASK EVERYONE YOU KNOW and contact me via email @.***
We need help. Will be willing to work or do what ever it takes to get some form of transportation. Thanks in advance for your support!!
Sent from Mailhttps://go.microsoft.com/fwlink/?LinkId=550986 for Windows 10
From: @.>
Sent: Saturday, January 1, 2022 7:58 AM
To: @.>
Cc: @.***>
Subject: Re: [microsoft/MLOpsPython] Include in Docker image necessary drivers for pyodbc (#319)
Hi @cindywenghttps://github.com/cindyweng, I was able to resolve this issue by creating a custom "run-config" environment for the ML-Ops Python,
We can install pyodbc and any other pip packages by going to ml_service/pipelines/diabetes_regression_build_train_pipeline.py
On line 37 and 38, we can replace the code with the one I provided below. This will create custom run-config and then manually we can enter the libraries to be used here.
##Defining the run configuration for the Pipeline to run
run_config = RunConfiguration()
The compute on which the pipeline would be running
and training the model
run_config.target = aml_compute
run_config.environment.python.conda_dependencies = \
CondaDependencies.create(
conda_packages=['pandas','scikit-learn','numpy','seaborn','matplotlib',
'pyodbc','openpyxl'],pip_packages=['azureml-sdk',
'azureml-dataset-runtime[fuse,pandas]','azureml-defaults',
'azureml-interpret','python-dotenv'],
pin_sdk_version=False)
Code Before:
[image]https://user-images.githubusercontent.com/42760941/147846668-c8453d3f-4710-45fc-91b8-db770a5d29c1.png
Code After:
[image]https://user-images.githubusercontent.com/42760941/147846672-30a9da73-dd22-4400-8508-37d9eda5556b.png
I hope this resolves this issue. Contributors: @muditmathur2020https://github.com/muditmathur2020 and @OmkarChoulwarhttps://github.com/OmkarChoulwar
Regards, Mudit Mathur Data Scientist
To summarize, adding any custom library in the environment is possible using the CondaDependencies in the azureml python. sdk.
—
Reply to this email directly, view it on GitHubhttps://github.com/microsoft/MLOpsPython/issues/319#issuecomment-1003562599, or unsubscribehttps://github.com/notifications/unsubscribe-auth/AODJGD5NP4IDCNIRFZFBGXDUT4CBLANCNFSM4P4QQ4QA.
Triage notifications on the go with GitHub Mobile for iOShttps://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675 or Androidhttps://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub.
You are receiving this because you are subscribed to this thread.Message ID: @.***>


Training Error

Hi all,
I am getting the following error while training. I have installed the latest tensorflow version. Was the blobs dictionary changed with new tensorflow ?
Any help is appreciated
Thanks
assign pretrain model weights to conv1_2
assign pretrain model biases to conv1_2
assign pretrain model weights to conv2_2
assign pretrain model biases to conv2_2
assign pretrain model weights to conv2_1
assign pretrain model biases to conv2_1
/scratch3/skoppura/Faster-RCNN_TF/tools/../lib/roi_data_layer/minibatch.py:100: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
  fg_inds, size=fg_rois_per_this_image, replace=False)
/scratch3/skoppura/Faster-RCNN_TF/tools/../lib/roi_data_layer/minibatch.py:120: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
  labels[fg_rois_per_this_image:] = 0
/scratch3/skoppura/Faster-RCNN_TF/tools/../lib/roi_data_layer/minibatch.py:176: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
  bbox_targets[ind, start:end] = bbox_target_data[ind, 1:]
/scratch3/skoppura/Faster-RCNN_TF/tools/../lib/roi_data_layer/minibatch.py:177: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
  bbox_inside_weights[ind, start:end] = cfg.TRAIN.BBOX_INSIDE_WEIGHTS
Traceback (most recent call last):
  File "./tools/train_net.py", line 96, in <module>
    max_iters=args.max_iters)
  File "/scratch3/skoppura/Faster-RCNN_TF/tools/../lib/fast_rcnn/train.py", line 222, in train_net
    sw.train_model(sess, max_iters)
  File "/scratch3/skoppura/Faster-RCNN_TF/tools/../lib/fast_rcnn/train.py", line 147, in train_model
    feed_dict={self.net.data: blobs['data'], self.net.im_info: blobs['im_info'], self.net.keep_prob: 0.5, \
KeyError: 'im_info'

I have some progress on this issue, but did not find any solution yet
The reason the key is not found is because, the blobs object is going to the else loop here insted of the if loop. This is in lib/roid_data_layer/minibatch.py. But when I tweak it to go the if loop, the very first  assertion fails. I infer that for loop has to be used for the case of classification step during training,
  blobs = {'data': im_blob}

    if cfg.TRAIN.HAS_RPN:
        assert len(im_scales) == 1, "Single batch only"
        assert len(roidb) == 1, "Single batch only"
        # gt boxes: (x1, y1, x2, y2, cls)
        gt_inds = np.where(roidb[0]['gt_classes'] != 0)[0]
        gt_boxes = np.empty((len(gt_inds), 5), dtype=np.float32)
        gt_boxes[:, 0:4] = roidb[0]['boxes'][gt_inds, :] * im_scales[0]
        gt_boxes[:, 4] = roidb[0]['gt_classes'][gt_inds]
        blobs['gt_boxes'] = gt_boxes
        blobs['im_info'] = np.array(
            [[im_blob.shape[1], im_blob.shape[2], im_scales[0]]],
            dtype=np.float32)
    else: # not using RPN
        # Now, build the region of interest and label blobs
        rois_blob = np.zeros((0, 5), dtype=np.float32)
        labels_blob = np.zeros((0), dtype=np.float32)
        bbox_targets_blob = np.zeros((0, 4 * num_classes), dtype=np.float32)
        bbox_inside_blob = np.zeros(bbox_targets_blob.shape, dtype=np.float32)
        # all_overlaps = []
        for im_i in xrange(num_images):
            labels, overlaps, im_rois, bbox_targets, bbox_inside_weights \
                = _sample_rois(roidb[im_i], fg_rois_per_image, rois_per_image,
                               num_classes)

            # Add to RoIs blob
            rois = _project_im_rois(im_rois, im_scales[im_i])
            batch_ind = im_i * np.ones((rois.shape[0], 1))
            rois_blob_this_image = np.hstack((batch_ind, rois))
            rois_blob = np.vstack((rois_blob, rois_blob_this_image))

            # Add to labels, bbox targets, and bbox loss blobs
            labels_blob = np.hstack((labels_blob, labels))
            bbox_targets_blob = np.vstack((bbox_targets_blob, bbox_targets))
            bbox_inside_blob = np.vstack((bbox_inside_blob, bbox_inside_weights))
            # all_overlaps = np.hstack((all_overlaps, overlaps))

Further progress:

when training with RPN: only one image per batch is allowed. go into the config file to change this if you'd like to go through the first if statement.

I am on the same boat, and I have the same problem as @chaituNova . Did you find a fix yet? @zacwellmer I modified my confs file through the .yaml file with the IMS_PER_BATCH flag set to 1, without any success.
I am also training a new dataset where hald the iamges have a person and the other half don't so the .xml data for those files has no   entry, with xmin,xmax,ymin,ymax entries. Might that be a problem since the error message is complaining about non-integer values in bounding boxes (although in my case, there is no bounding box for a specific image)?
I'm posting my complete error message:
Instructions for updating:
Use `tf.global_variables_initializer` instead.
Loading pretrained model weights from data/pretrain_model/VGG_imagenet.npy
assign pretrain model weights to conv5_1
assign pretrain model biases to conv5_1
assign pretrain model weights to fc6
assign pretrain model biases to fc6
assign pretrain model weights to conv5_3
assign pretrain model biases to conv5_3
assign pretrain model weights to fc7
assign pretrain model biases to fc7
ignore fc8
ignore fc8
assign pretrain model weights to conv5_2
assign pretrain model biases to conv5_2
assign pretrain model weights to conv4_1
assign pretrain model biases to conv4_1
assign pretrain model weights to conv4_2
assign pretrain model biases to conv4_2
assign pretrain model weights to conv4_3
assign pretrain model biases to conv4_3
assign pretrain model weights to conv3_3
assign pretrain model biases to conv3_3
assign pretrain model weights to conv3_2
assign pretrain model biases to conv3_2
assign pretrain model weights to conv3_1
assign pretrain model biases to conv3_1
assign pretrain model weights to conv1_1
assign pretrain model biases to conv1_1
assign pretrain model weights to conv1_2
assign pretrain model biases to conv1_2
assign pretrain model weights to conv2_2
assign pretrain model biases to conv2_2
assign pretrain model weights to conv2_1
assign pretrain model biases to conv2_1
/media/arturo/AlphaStorage/Arturo/SCORCH_ComputerVision/Faster-RCNN_TF/tools/../lib/rpn_msr/proposal_target_layer_tf.py:89: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
  bbox_targets[ind, start:end] = bbox_target_data[ind, 1:]
/media/arturo/AlphaStorage/Arturo/SCORCH_ComputerVision/Faster-RCNN_TF/tools/../lib/rpn_msr/proposal_target_layer_tf.py:90: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
  bbox_inside_weights[ind, start:end] = cfg.TRAIN.BBOX_INSIDE_WEIGHTS
Traceback (most recent call last):
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/script_ops.py", line 85, in __call__
    ret = func(*args)
  File "/media/arturo/AlphaStorage/Arturo/SCORCH_ComputerVision/Faster-RCNN_TF/tools/../lib/rpn_msr/anchor_target_layer_tf.py", line 116, in anchor_target_layer
    argmax_overlaps = overlaps.argmax(axis=1)
ValueError: attempt to get argmax of an empty sequence
W tensorflow/core/framework/op_kernel.cc:975] Internal: Failed to run py callback pyfunc_0: see error log.
W tensorflow/core/framework/op_kernel.cc:975] Internal: Failed to run py callback pyfunc_0: see error log.
	 [[Node: rpn-data/PyFunc = PyFunc[Tin=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_INT32, DT_INT32], Tout=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT], token="pyfunc_0", _device="/job:localhost/replica:0/task:0/cpu:0"](rpn_cls_score/rpn_cls_score/_143, _recv_Placeholder_2_0, _recv_Placeholder_1_0, _recv_Placeholder_0, rpn-data/PyFunc/input_4, rpn-data/PyFunc/input_5)]]
Traceback (most recent call last):
  File "./tools/train_net.py", line 96, in <module>
    max_iters=args.max_iters)
  File "/media/arturo/AlphaStorage/Arturo/SCORCH_ComputerVision/Faster-RCNN_TF/tools/../lib/fast_rcnn/train.py", line 225, in train_net
    sw.train_model(sess, max_iters)
  File "/media/arturo/AlphaStorage/Arturo/SCORCH_ComputerVision/Faster-RCNN_TF/tools/../lib/fast_rcnn/train.py", line 164, in train_model
    run_metadata=run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 766, in run
    run_metadata_ptr)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 964, in _run
    feed_dict_string, options, run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1014, in _do_run
    target_list, options, run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1034, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InternalError: Failed to run py callback pyfunc_0: see error log.
	 [[Node: rpn-data/PyFunc = PyFunc[Tin=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_INT32, DT_INT32], Tout=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT], token="pyfunc_0", _device="/job:localhost/replica:0/task:0/cpu:0"](rpn_cls_score/rpn_cls_score/_143, _recv_Placeholder_2_0, _recv_Placeholder_1_0, _recv_Placeholder_0, rpn-data/PyFunc/input_4, rpn-data/PyFunc/input_5)]]
	 [[Node: Gather_1/_163 = _HostRecv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/gpu:0", send_device="/job:localhost/replica:0/task:0/cpu:0", send_device_incarnation=1, tensor_name="edge_954_Gather_1", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/gpu:0"]()]]

Caused by op u'rpn-data/PyFunc', defined at:
  File "./tools/train_net.py", line 91, in <module>
    network = get_network(args.network_name)
  File "/media/arturo/AlphaStorage/Arturo/SCORCH_ComputerVision/Faster-RCNN_TF/tools/../lib/networks/factory.py", line 30, in get_network
    return networks.VGGnet_train()
  File "/media/arturo/AlphaStorage/Arturo/SCORCH_ComputerVision/Faster-RCNN_TF/tools/../lib/networks/VGGnet_train.py", line 20, in __init__
    self.setup()
  File "/media/arturo/AlphaStorage/Arturo/SCORCH_ComputerVision/Faster-RCNN_TF/tools/../lib/networks/VGGnet_train.py", line 58, in setup
    .anchor_target_layer(_feat_stride, anchor_scales, name = 'rpn-data' ))
  File "/media/arturo/AlphaStorage/Arturo/SCORCH_ComputerVision/Faster-RCNN_TF/tools/../lib/networks/network.py", line 25, in layer_decorated
    layer_output = op(self, layer_input, *args, **kwargs)
  File "/media/arturo/AlphaStorage/Arturo/SCORCH_ComputerVision/Faster-RCNN_TF/tools/../lib/networks/network.py", line 169, in anchor_target_layer
    rpn_labels,rpn_bbox_targets,rpn_bbox_inside_weights,rpn_bbox_outside_weights = tf.py_func(anchor_target_layer_py,[input[0],input[1],input[2],input[3], _feat_stride, anchor_scales],[tf.float32,tf.float32,tf.float32,tf.float32])
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/script_ops.py", line 192, in py_func
    input=inp, token=token, Tout=Tout, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_script_ops.py", line 40, in _py_func
    name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 759, in apply_op
    op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2240, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1128, in __init__
    self._traceback = _extract_stack()

InternalError (see above for traceback): Failed to run py callback pyfunc_0: see error log.
	 [[Node: rpn-data/PyFunc = PyFunc[Tin=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_INT32, DT_INT32], Tout=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT], token="pyfunc_0", _device="/job:localhost/replica:0/task:0/cpu:0"](rpn_cls_score/rpn_cls_score/_143, _recv_Placeholder_2_0, _recv_Placeholder_1_0, _recv_Placeholder_0, rpn-data/PyFunc/input_4, rpn-data/PyFunc/input_5)]]
	 [[Node: Gather_1/_163 = _HostRecv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/gpu:0", send_device="/job:localhost/replica:0/task:0/cpu:0", send_device_incarnation=1, tensor_name="edge_954_Gather_1", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/gpu:0"]()]]

@chaituNova  I meet the same problem with you. My solution is to try both flipping the statements in if.. .else... you mentioned and @zacwellmer 's changing the value of IMS_PER_BATCH.

I am hitting a similar problem, but setting IMS_PER_BATCH=1 and  HAS_RPN=True did not fix it unfortunately.

W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties:
name: GeForce GTX 1070
major: 6 minor: 1 memoryClockRate (GHz) 1.683
pciBusID 0000:03:00.0
Total memory: 7.92GiB
Free memory: 7.39GiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0:   Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1070, pci bus id: 0000:03:00.0)
Computing bounding-box regression targets...
bbox target means:
[[ 0.  0.  0.  0.]
[ 0.  0.  0.  0.]
[ 0.  0.  0.  0.]]
[ 0.  0.  0.  0.]
bbox target stdevs:
[[ 0.1  0.1  0.2  0.2]
[ 0.1  0.1  0.2  0.2]
[ 0.1  0.1  0.2  0.2]]
[ 0.1  0.1  0.2  0.2]
Normalizing targets
done
Solving...
/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gradients_impl.py:91: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.
"Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
Loading pretrained model weights from data/pretrain_model/VGG_imagenet.npy
assign pretrain model weights to conv5_1
assign pretrain model biases to conv5_1
assign pretrain model weights to fc6
assign pretrain model biases to fc6
assign pretrain model weights to conv5_3
assign pretrain model biases to conv5_3
assign pretrain model weights to fc7
assign pretrain model biases to fc7
ignore fc8
ignore fc8
assign pretrain model weights to conv5_2
assign pretrain model biases to conv5_2
assign pretrain model weights to conv4_1
assign pretrain model biases to conv4_1
assign pretrain model weights to conv4_2
assign pretrain model biases to conv4_2
assign pretrain model weights to conv4_3
assign pretrain model biases to conv4_3
assign pretrain model weights to conv3_3
assign pretrain model biases to conv3_3
assign pretrain model weights to conv3_2
assign pretrain model biases to conv3_2
assign pretrain model weights to conv3_1
assign pretrain model biases to conv3_1
assign pretrain model weights to conv1_1
assign pretrain model biases to conv1_1
assign pretrain model weights to conv1_2
assign pretrain model biases to conv1_2
assign pretrain model weights to conv2_2
assign pretrain model biases to conv2_2
assign pretrain model weights to conv2_1
assign pretrain model biases to conv2_1
Traceback (most recent call last):
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/script_ops.py", line 85, in call
ret = func(*args)
File "/home/scott/code/Faster-RCNN_TF/tools/../lib/rpn_msr/anchor_target_layer_tf.py", line 116, in anchor_target_layer
argmax_overlaps = overlaps.argmax(axis=1)
ValueError: attempt to get argmax of an empty sequence
W tensorflow/core/framework/op_kernel.cc:993] Internal: Failed to run py callback pyfunc_0: see error log.
W tensorflow/core/framework/op_kernel.cc:993] Internal: Failed to run py callback pyfunc_0: see error log.
[[Node: rpn-data/PyFunc = PyFunc[Tin=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_INT32, DT_INT32], Tout=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT], token="pyfunc_0", _device="/job:localhost/replica:0/task:0/cpu:0"](rpn_cls_score/rpn_cls_score/_143, _recv_Placeholder_2_0, _recv_Placeholder_1_0, _recv_Placeholder_0, rpn-data/PyFunc/input_4, rpn-data/PyFunc/input_5)]]
W tensorflow/core/framework/op_kernel.cc:993] Internal: Failed to run py callback pyfunc_0: see error log.
[[Node: rpn-data/PyFunc = PyFunc[Tin=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_INT32, DT_INT32], Tout=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT], token="pyfunc_0", _device="/job:localhost/replica:0/task:0/cpu:0"](rpn_cls_score/rpn_cls_score/_143, _recv_Placeholder_2_0, _recv_Placeholder_1_0, _recv_Placeholder_0, rpn-data/PyFunc/input_4, rpn-data/PyFunc/input_5)]]
Traceback (most recent call last):
File "./tools/train_net.py", line 96, in
max_iters=args.max_iters)
File "/home/scott/code/Faster-RCNN_TF/tools/../lib/fast_rcnn/train.py", line 223, in train_net
sw.train_model(sess, max_iters)
File "/home/scott/code/Faster-RCNN_TF/tools/../lib/fast_rcnn/train.py", line 162, in train_model
run_metadata=run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 767, in run
run_metadata_ptr)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 965, in _run
feed_dict_string, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1015, in _do_run
target_list, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1035, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InternalError: Failed to run py callback pyfunc_0: see error log.
[[Node: rpn-data/PyFunc = PyFunc[Tin=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_INT32, DT_INT32], Tout=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT], token="pyfunc_0", _device="/job:localhost/replica:0/task:0/cpu:0"](rpn_cls_score/rpn_cls_score/_143, _recv_Placeholder_2_0, _recv_Placeholder_1_0, _recv_Placeholder_0, rpn-data/PyFunc/input_4, rpn-data/PyFunc/input_5)]]
Caused by op u'rpn-data/PyFunc', defined at:
File "./tools/train_net.py", line 91, in
network = get_network(args.network_name)
File "/home/scott/code/Faster-RCNN_TF/tools/../lib/networks/factory.py", line 30, in get_network
return networks.VGGnet_train()
File "/home/scott/code/Faster-RCNN_TF/tools/../lib/networks/VGGnet_train.py", line 20, in init
self.setup()
File "/home/scott/code/Faster-RCNN_TF/tools/../lib/networks/VGGnet_train.py", line 58, in setup
.anchor_target_layer(_feat_stride, anchor_scales, name = 'rpn-data' ))
File "/home/scott/code/Faster-RCNN_TF/tools/../lib/networks/network.py", line 25, in layer_decorated
layer_output = op(self, layer_input, *args, **kwargs)
File "/home/scott/code/Faster-RCNN_TF/tools/../lib/networks/network.py", line 169, in anchor_target_layer
rpn_labels,rpn_bbox_targets,rpn_bbox_inside_weights,rpn_bbox_outside_weights = tf.py_func(anchor_target_layer_py,[input[0],input[1],input[2],input[3], _feat_stride, anchor_scales],[tf.float32,tf.float32,tf.float32,tf.float32])
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/script_ops.py", line 192, in py_func
input=inp, token=token, Tout=Tout, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_script_ops.py", line 40, in _py_func
name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 763, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2395, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1264, in init
self._traceback = _extract_stack()
InternalError (see above for traceback): Failed to run py callback pyfunc_0: see error log.
[[Node: rpn-data/PyFunc = PyFunc[Tin=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_INT32, DT_INT32], Tout=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT], token="pyfunc_0", _device="/job:localhost/replica:0/task:0/cpu:0"](rpn_cls_score/rpn_cls_score/_143, _recv_Placeholder_2_0, _recv_Placeholder_1_0, _recv_Placeholder_0, rpn-data/PyFunc/input_4, rpn-data/PyFunc/input_5)]]

same problem here~~~

Hi,  I have same issue.  Any one found the fix yet?

Had the same issue and figured out after some debugging that configs are not loaded properly from faster_rcnn_end2end.yml (even though it appears in log). As a workaround updated config.py with the values of faster_rcnn_end2end.yml. Check https://github.com/cdathuraliya/Faster-RCNN_TF/commit/c3f8f57976a7341c14bbaef6eb70bd3cbedb3d94 to see the changes. This worked for me.

Had this same issue:
...Faster-RCNN_TF/tools/../lib/rpn_msr/anchor_target_layer_tf.py", line 116, in anchor_target_layer
argmax_overlaps = overlaps.argmax(axis=1)
ValueError: attempt to get argmax of an empty sequence
...

Changing config.py did not worked for me.
I found that some images in my custom dataset had no annotation, and that was the cause of this particular problem.
As a quick work-around, removing those images from my training list worked for me

Has anyone made further progress with this issue?
I'm getting this on every image in a custom dataset.

i don't know why,but
modify __C.TRAIN.IMS_PER_BATCH = 1 can fix it.


SmoothQuant vs AWQ which one is faster?

Question
We are very interested in two post-training quantization papers from han lab!
SmoothQuant use W8A8 for efficient GPU computation.
AWQ uses W4/3A16 for lower memory requirements and higher memory throughput.
But which one is faster in actual production?
If you have any data about this, could you share it with us?

Question
We are very interested in two post-training quantization papers from han lab!
SmoothQuant use W8A8 for efficient GPU computation. AWQ uses W4/3A16 for lower memory requirements and higher memory throughput.
But which one is faster in actual production? If you have any data about this, could you share it with us?

W4A16 is the fastest. I believe this is discussed in the paper, something along the lines of “weights make up the majority of delay”. Most layers in transformers are linear layers, so naturally you will see a large benefit from quantizing them.
I don’t have benchmarks to compare against SmoothQuant as it seems AWQ is preferred by the authors due to usability and speed with TinyChat.

Hi @codertimo , usually W8A8 (SmoothQuant) is better for compute-bounded scenarios (e.g., large batch size, targeting large throughput), and W4A16 (AWQ) is better for memory-bounded scenarios (smaller batch size, lower latency). Let me know if you have more questions.


Add register init to RemoveWires dependencies

Adds a dependency to RemoveWires for registers that do not initialize themselves. (Oddly, adding a dependency for self-initializing registers causes InfoSpec to fail.)
Fixes #1077

 Thank you for your submission, we really appreciate it. Like many open source projects, we ask that you all sign our Contributor License Agreement before we can accept your contribution.2 out of 3 committers have signed the CLA.:white_check_mark: seldridge:white_check_mark: azidar:x: mergify-botmergify-bot seems not to be a GitHub user. You need a GitHub account to be able to sign the CLA. If you have already a GitHub account, please add the email address used for this commit to your account.You have signed the CLA already but the status is still pending? Let us recheck it.

✅ seldridge
✅ azidar
❌ mergify-bot

lol

lol so mergify can just merge anyway? Nice


unsaved tabs crashing

Azure Data Studio Version:

Version: 1.9.0 (system setup)
Commit: 78a42e1d112ae3231777722b51eaf44f83ddbe55
Date: 2019-07-10T04:31:36.998Z
VS Code 1.34.0
Electron: 3.1.8
Chrome: 66.0.3359.181
Node.js: 10.2.0
V8: 6.6.346.32
OS: Windows_NT x64 10.0.17134
Steps to Reproduce:

open a new sql tab
write some tsql code and connected to an instance
after some idle time, the pop with the error message shows up and the tab closes automatically

Error: Unable to open 'SQLQuery_1 - *****...grated)': Cannot read property 'slice' of undefined.
Note: the problem occurs rarely.

@brentwc

I would say this occurs for me about once to twice a week?  This will also happen on tabs where the query has been saved previously.
Version: 1.10.0 (user setup)
Commit: 5fdb967ffcea41df0335ec17cfa396a694bb174e
Date: 2019-08-14T18:28:10.311Z
VS Code 1.37.0
Electron: 4.2.7
Chrome: 69.0.3497.128
Node.js: 10.11.0
V8: 6.9.427.31-electron.0
OS: Windows_NT x64 10.0.18362

could be related #7177

This just happened to me. Quite frustrating.

frequently encountering this issue.

Is there any way to open the closed file?

Still happing.

Hi Anthony, thank you for responding. I just had another tab crash, but the Window log does not show any errors. SQLQuery_3 was the tab that crashed, right after I closed SQLQuery_5. Here is the log anyway just in case, and a screenshot of the developer tools error.
2020-02-04 14:01:55.861] [renderer1] [info] Creating new connection untitled:SQLQuery_3
[2020-02-04 14:01:55.861] [renderer1] [info] Adding connection untitled:SQLQuery_3
[2020-02-04 14:01:55.861] [renderer1] [info] Successfully added connection untitled:SQLQuery_3
[2020-02-04 14:13:53.211] [renderer1] [info] Creating new connection untitled:SQLQuery_5
[2020-02-04 14:13:53.212] [renderer1] [info] Adding connection untitled:SQLQuery_5
[2020-02-04 14:13:53.214] [renderer1] [info] Successfully added connection untitled:SQLQuery_5
[2020-02-04 15:29:38.564] [renderer1] [info] Deleting connection untitled:SQLQuery_1
[2020-02-04 15:30:26.200] [renderer1] [info] Deleting connection untitled:SQLQuery_5
[2020-02-04 15:30:26.230] [renderer1] [info] Deleting connection untitled:SQLQuery_3
[2020-02-04 15:30:57.877] [renderer1] [info] Deleting connection untitled:SQLQuery_2

This is the JS code that it says is failing. Not sure if this is relevant or if it helps, but I thought I would post it just in case.

I'm seeing this issue in ADS as well.  I'll have an (unsaved) query editor open with results, switch to another query editor or dashboard, then when I attempt to return to the query editor the tab dissapears and I get "cannot read property 'slice' of undefined"
Same dev console logs as above except my instance is a system install.
Version: 1.15.0 (system setup)
Commit: e57eb999fc5f1e000477da8fe0b8679ff0bfd57b
Date: 2020-02-12T20:48:43.389Z
VS Code: 1.42.0
Electron: 6.1.6
Chrome: 76.0.3809.146
Node.js: 12.4.0
V8: 7.6.303.31-electron.0
OS: Windows_NT x64 10.0.19569

Hi @kburtram, is there any update on this issue? I am currently unable to use the software due to potential loss of work from this bug. Thank you.

@stelth240 unfortunately the error message provided doesn't give enough information to go on.  Basically it says we're calling slice somewhere with a null object, which could be one of numerous places in the code.  The Zone code is a wrapper to handle calls that cross an isolation boundary and isn't the actual source of the error.  I haven't been able to repro this yet, but some mentioned that perhaps force killing Azure Data Studio could force this bug to occur, but I haven't had a chance to test that out.  I'll take a look again to try to repro.

@kburtram Thanks for the reply. I actually did find where I think the error is occurring based on the error message that is thrown. Maybe that will help trace back to the root issue. It is in the "vs" folder under "src", so is it something that the vscode team would need to fix?
Here is the line with the error that is emitted:
https://github.com/microsoft/azuredatastudio/blob/5613a97fae6b5cb37f079a67c33c4694f0046c17/src/vs/workbench/browser/parts/editor/editorGroupView.ts#L1064

error also occurs on mac.

I'm seeing the same issue. It seems to mostly happen on one file which was saved previously. I can open the file, run the query, then change tabs and leave it for a while - at which point when I try switching back to the tab it will crash with the same error.

I'm getting a ton of stack traces like the one below in Log (Window) but I don't know if it's related.
[2020-09-15 20:16:30.608] [renderer1] [error] TreeError [MarkersView] Tree element not found: [object Object]: Error: TreeError [MarkersView] Tree element not found: [object Object]
    at new i (file:///C:/Users/USER_NAME/AppData/Local/Programs/Azure Data Studio/resources/app/out/vs/workbench/workbench.desktop.main.js:127:950) [<root>]
    at t.ObjectTreeModel.getElementLocation (file:///C:/Users/USER_NAME/AppData/Local/Programs/Azure Data Studio/resources/app/out/vs/workbench/workbench.desktop.main.js:218:613) [<root>]
    at t.ObjectTreeModel.setCollapsed (file:///C:/Users/USER_NAME/AppData/Local/Programs/Azure Data Studio/resources/app/out/vs/workbench/workbench.desktop.main.js:217:855) [<root>]
    at j.expand (file:///C:/Users/USER_NAME/AppData/Local/Programs/Azure Data Studio/resources/app/out/vs/workbench/workbench.desktop.main.js:1509:277) [<root>]
    at H.autoReveal (file:///C:/Users/USER_NAME/AppData/Local/Programs/Azure Data Studio/resources/app/out/vs/workbench/workbench.desktop.main.js:8931:818) [<root>]
    at H.onActiveEditorChanged (file:///C:/Users/USER_NAME/AppData/Local/Programs/Azure Data Studio/resources/app/out/vs/workbench/workbench.desktop.main.js:8928:842) [<root>]
    at file:///C:/Users/USER_NAME/AppData/Local/Programs/Azure Data Studio/resources/app/out/vs/workbench/workbench.desktop.main.js:8927:715 [<root>]
    at file:///C:/Users/USER_NAME/AppData/Local/Programs/Azure Data Studio/resources/app/out/vs/workbench/workbench.desktop.main.js:190:827 [<root>]
    at c.fire (file:///C:/Users/USER_NAME/AppData/Local/Programs/Azure Data Studio/resources/app/out/vs/workbench/workbench.desktop.main.js:194:423) [<root>]
    at F.doHandleActiveEditorChangeEvent (file:///C:/Users/USER_NAME/AppData/Local/Programs/Azure Data Studio/resources/app/out/vs/workbench/workbench.desktop.main.js:7989:92) [<root>]
    at F.handleActiveEditorChange (file:///C:/Users/USER_NAME/AppData/Local/Programs/Azure Data Studio/resources/app/out/vs/workbench/workbench.desktop.main.js:7988:882) [<root>]
    at file:///C:/Users/USER_NAME/AppData/Local/Programs/Azure Data Studio/resources/app/out/vs/workbench/workbench.desktop.main.js:7989:202 [<root>]
    at c.fire (file:///C:/Users/USER_NAME/AppData/Local/Programs/Azure Data Studio/resources/app/out/vs/workbench/workbench.desktop.main.js:194:423) [<root>]
    at file:///C:/Users/USER_NAME/AppData/Local/Programs/Azure Data Studio/resources/app/out/vs/workbench/workbench.desktop.main.js:8228:665 [<root>]

The most recent error in that window log after the problem occurs and I see the error pop-up, though, are these:
[```
2020-09-15 21:22:24.781] [renderer1] [error] An unknown error occurred. Please consult the log for more details.
[2020-09-15 21:22:33.585] [renderer1] [error] An unknown error occurred. Please consult the log for more details.
[2020-09-15 21:23:37.538] [renderer1] [error] An unknown error occurred. Please consult the log for more details.
[2020-09-15 21:23:41.789] [renderer1] [error] An unknown error occurred. Please consult the log for more details.
[2020-09-15 21:23:49.722] [renderer1] [error] An unknown error occurred. Please consult the log for more details.
[2020-09-15 21:23:55.931] [renderer1] [error] An unknown error occurred. Please consult the log for more details.
Of course this is IN the log, so I'm not sure what I'm supposed to consult for more details.

this happens to me couple times a week :*(

This has happened to me seemingly randomly over the past year or so, and just happened to me again twice today, both on unsaved tabs. This time it was a tab with the same name twice.
The window log was useless and had nothing in it, but the developer tools gave me the following error:

I still don't know how to reproduce this issue, but hopefully this screenshot of the error helps track down this issue.

It happened again just now. Similar error as last time, but with a slightly different stack trace:

I had just saved another tab and was switching back to "SQLQuery_1" to check something when that tab crashed and gave me that error.

Happened again, just now. Here's the stack trace this time:

Looks like it's the same as last time.
So what happened before this error?
I went to a tab I had an opened saved file to that I hadn't switched to for a few hours, and did a Save As to save it with a different name so I could make some tweaks to it for a task I'm working on, and when I switched back to the last unsaved tab I was in (with Ctrl+Tab), that tab then crashed.
I had a similar sequence of events for the last crash I reported, so hopefully this helps narrow things down a bit.

Any additional logs or details that would help troubleshoot this?  I have it re-occur frequently enough in ADS that it is unproductive.

Likewise. This issue occurs for me several times a week.

Likewise. This issue occurs for me several times a week.

happening to me to... very frustrating...
Version: 1.25.1

happening to me to... very frustrating...
Version: 1.25.1

Hi, I get the same error, but not only on unsaved tabs:
Unable to open 'playground.sql - 35.242...l_user)': Cannot read property 'slice' of undefined.
Version: 1.25.1
Commit: e7eacc32c0d20bd4da843320c3bfac231b308c98
Date: 2020-12-11T01:51:41.708Z
VS Code: 1.48.0
Electron: 9.3.0
Chrome: 83.0.4103.122
Node.js: 12.14.1
V8: <IP_ADDRESS>-electron.0
OS: Linux x64 5.4.0-65-generic

Happening to me several times a day.  I now save every query every on every change.  Very annoying.

Closing this as a duplicate of https://github.com/microsoft/azuredatastudio/issues/14929 - see that for some more information on additional logging we added recently to try and track this down.

Reopening since we're still getting duplicate reports of this.  #14929 is closed so reopening the earliest report with investigation history.

I also ran into this issue today, previously I fixed some places, looking at my stack trace, it is the chart that is having issue, i'll have a fix for it.

This just happened to me again as well. I have a lot of errors and warnings in my console log, and I have uninstalled and reinstalled ADS quite recently. I have attached the log as well as a screenshot of the slice error. Thank you.
Console Log 2021-09-09.log

@stelth240 Do you recall what you were doing when this happened? Was it just idle as the original issue described or were you doing something like switching tabs?

@Charles-Gagnon I actually remember the exact steps I took. I had three tabs open (1, 2, 3). Tabs 1 and 2 were connected to one server, and tab 3 was connected to a different server. Tab 2 was just opened minutes before the issue occurred, and I had somewhat recently clicked on both tabs 1 and 3 before tab 2 was open.
I finished my work in tab 2, clicked the X in the corner, and said Don't Save. Immediately, both tabs 2 and 3 closed, with the error occurring on tab 3.
There are also times when I just switch from one tab to another, and the tab I am switching to closes immediately. I believe that is what happened with the scenario above, but it switched to tab 3 as a result of me closing tab 2.
Hope this helps.

Running version 1.32.0, I just got this error again:

I was just closing several saved tabs I no longer needed open and when I closed the last of them, it switched to one of my unsaved tabs and that tab crashed. This is with SQL Server. I believe the unsaved tab is one I hadn't looked at in a while.
Unfortunately I still haven't been able to consistently reproduce this issue or pin down a specific sequence of events that lead to it. However, it does seem to occur most often when I have closed another tab and ADS has then automatically switched to the tab that then crashes.

@Charles-Gagnon I received the error again today, but I was doing something different than I have done in the past. I was dragging a tab from its own column back into the main tabs. I have attached the stack trace screenshot and the console log. Thanks!
Console Log 2021-09-13.log

Hi @alanrenmsft ,
I am still receiving the same error as before, and the tabs are still closing on me. I am currently at version 1.33.1. Below is a screenshot of the error in my stack trace. I had closed another tab and clicked Don't Save. It ended up closing both tabs at the same time, and the error appeared as normal. I was just in that tab shortly before this occurred, maybe 30 minutes before the error.
I know this issue was closed, but the improvement does not seem to be working as expected, at least from what I just experienced.
Thank you for your help with this isue.

@stelth240 by looking at the call stack, it should have been caught by my previous fix, are you sure you are using 1.33.1 when the error occurred? anyway, I've made another change to stop if from crashing.

@alanrenmsft Hi Alan. Yes, I am sure. Below is a screenshot of the About dialog. I have not updated since the error occurred, so this is the version I was on. A tab actually closed on me again last week as well. I did not grab the stack trace that time, though it looked the same as the other times. Thank you for looking into this; it is stopping my whole team from using ADS since they are having the same issue and do not want to lose their work.

@stelth240 our new release will be available in a week or so, please give it a try and definitely let us know if the problem is still there. You can also use the insider build which already have my most recent fix.

@alanrenmsft Thank you. I will wait for the new release to be available, and check it then. I really appreciate the help with this issue.

@alanrenmsft It has been a while, but I finally received the error that the connection is no longer valid and to save and reopen the tab. I did not lose my work and was able to copy/paste the query without losing it. I really appreciate the help getting this workaround in place!

@stelth240 that's great!


Make resolveAssetSource public

When building a native component which takes an image reference as a prop, resolveAssetSource needs to be called on the image reference. If this isn't done, the native component may receive the opaque type returned by require (e.g. require('./foo.png')) which is useless to the native component. resolveAssetSource is used by builtin components that take image references such as Image, WebView and MapView.
This change makes resolveAssetSource public so that third-party native components can correctly handle image references.
Test plan (required)
Verified that Image.resolveAssetSource works in a test app. Also, my team is using this change in our app.
Adam Comella
Microsoft Corp.

Ok, looks great!
@facebook-github-bot shipit


A large amount of irrelevant information is generated

Why did I only ask what 1+1 equals, and it added a bunch of irrelevant information in addition to answering that it equals 2?

Well, the main reason is that, the first public releases are just base models. You can try to finetune on public datasets manually. Or you can wait for our upcoming chat version models.

Thank you


Fallbacks if the key could not be resolved

Hi,
it is possible to implement some fallbacks if a key could not be resolved?
The following logic would be conceivable:

If the key in the actual language not available, makes a fallback to defaultLanguage.
If even defaultLanguage[key] doesn't resolve, it returns the key.

Put this code to Translator instant function before line 263:
var t;
if (!this.translations[language] || !this.translations[language][key]) {
	this.logHandler.info(this.generateMessage("missing", { key: key, language: language }));
	if (language === this.config.defaultLanguage) {
        // This key is not available in the default language
		return key;
	}
	else {
	    // Use default language to translate the key
		t = this.instant(key, params, this.config.defaultLanguage);
	}
}
else {
	t = this.translations[language][key];
}

makes sense - especially when using keys that don't read well. feel free to open a MR.

Done. I hope everything fits.

Oh, I see: "Coverage decreased".
I try to complete the tests.

@javix thx for your contribution


how to search files from upper level directory?

for example:
current in directory dir2, but want to search file 1 in dir1, what can I do?

:LeaderfFile ..

If directory has multiple levels, must run like LeaderfFile ../../.. or LeaderfFile ../.. ?
If so, that is not convenient as ctrlp does.

It can do whatever ctrlp can do.

yes, it can do. But how can I do it without knowing directory levels?

I read leaderf.txt and found Lf_RootMarkers. How can I search for files from Lf_RootMarkers directory at any time?

let g:Lf_WorkingDirectoryMode = 'Ac' work for me.


[WIP] Improve Documentation

Changelog

Improve README instructions
Add Table of contents
Format headings according to fit with TOC
Add contributors (having more than one commit)
Remove reduncant 'Python Codes and Examples' section from README
Add all-contributors in packages
Make indent sizes as 4 as per Python's convention

Adding Docstrings in the next commit to this PR
Checklist:

[x] Have you followed the guidelines in our Contributing document?
[x] Have you checked to ensure there aren't other open Pull Requests for the same update/change?
[x] Have you successfully ran tests with your changes locally?
[x] Have you added an explanation of what your changes do and why you'd like us to include them?

Issues Solved
Fixes #165

View rendered README.md

Changelog

Add docstrings as per PEP8 (while also keeping PEP20 in mind).
Updated some previous commits and docstring as per conventions of PEP257 (Apparently, the only way to commit in Python is through the # character. Comments created using quotes are actually just unassigned strings which are discouraged. Also, docstrings are made using """triple double quotes""").
Changed wildcard imports to explicit (Explicit is better than implicit) following PEP328.
Add new demo file


[Enhancement] Use tags instead of item IDs where appropriate.

There are a number of cases where changing from using a specific item ID to a tag would automatically add compatibility with several new mods.
For example, if Arcane Gold was to use the forge:ingots/arcane_gold tag, the material would be compatible with the Arcane Gold from both Eidolon and Forbidden and Arcanus.
Similarly, forge:ingots/pewter, forge:ingots/brass, and forge:gems/moonstone all have tags included in their respective mods.

This has been fixed for a while, just forgot to close the issue.


Bump closex to version 1.0.0 🎉

Breaking changes are only to the mock client.
Mock client now more closely resembles the actual api when a
lead/opportunity/custom field etc is not found.
If before you were asserting on the result of the mock client when something is not found, your
tests will fail. Ensure you check the docs to see the new return values.
See here for changes: https://github.com/nested-tech/closex/pull/22

On looking at the changes, I think there's no need to bump to 1.0.0 here a minor bump is fine; it just gives more accurate test mocks. Yes it'll break people's tests, but it won't break their running application usage.


Should Add Possible Values Hints for Enum Type

I think Composer should provide more hints for what kind of the enum value could be set to a specific field.
For example, in Activity Received Trigger, If user want to set the 'activity type' field, maybe he would be confused because this is a enum type. All possible values are
"description": "Contains the activity type. Possible values include: 'message', 'contactRelationUpdate',\n'conversationUpdate', 'typing', 'endOfConversation', 'event', 'invoke', 'deleteUserData',\n'messageUpdate', 'messageDelete', 'installationUpdate', 'messageReaction', 'suggestion',\n'trace', 'handoff'",

But currently composer is just showing the description in sdk.schema.

@sangwoohaan be aware of this.

We'll continue to add updated copy to schema definitions and track them through other work items


Online Boutique demo deployed with px CLI in CrashLoopBackOff

Describe the bug
Two services in the Online Boutique demo deployed using px demo deploy px-online-boutique are in CrashLoopBackOff with the following errors:
emailservice
Profiler enabled.
Tracing enabled.
Debugger enabled.
{"severity":"info","time":1673536345440,"pid":1,"hostname":"currencyservice-5c7f7b6ddf-hxzh2","name":"currencyservice-server","message":"Starting gRPC server on port 7000...","v":1}
(node:1) [changeLevelName_deprecation] Warning: The changeLevelName option is deprecated and will be removed in v7. Use levelKey instead.
(Use `node --trace-warnings ...` to show where the warning was created)
{"severity":"info","time":1673536345539,"pid":1,"hostname":"currencyservice-5c7f7b6ddf-hxzh2","name":"currencyservice-server","message":"CurrencyService gRPC server started on port 7000","v":1}
/usr/src/app/node_modules/@google-cloud/profiler/build/src/index.js:120
        throw new Error('Project ID must be specified in the configuration');
              ^

Error: Project ID must be specified in the configuration
    at initConfigMetadata (/usr/src/app/node_modules/@google-cloud/profiler/build/src/index.js:120:15)
    at processTicksAndRejections (node:internal/process/task_queues:96:5)
    at async createProfiler (/usr/src/app/node_modules/@google-cloud/profiler/build/src/index.js:158:26)
    at async Object.start (/usr/src/app/node_modules/@google-cloud/profiler/build/src/index.js:182:22)

paymentservice
Profiler enabled.
Tracing enabled.
Debugger enabled.
(node:1) [changeLevelName_deprecation] Warning: The changeLevelName option is deprecated and will be removed in v7. Use levelKey instead.
(Use `node --trace-warnings ...` to show where the warning was created)
(node:1) [changeLevelName_deprecation] Warning: The changeLevelName option is deprecated and will be removed in v7. Use levelKey instead.
{"severity":"info","time":1673536399035,"pid":1,"hostname":"paymentservice-6d9b7cc6bf-kqjlc","name":"paymentservice-server","message":"PaymentService gRPC server started on port 50051","v":1}
/usr/src/app/node_modules/@google-cloud/profiler/build/src/index.js:120
        throw new Error('Project ID must be specified in the configuration');
              ^

Error: Project ID must be specified in the configuration
    at initConfigMetadata (/usr/src/app/node_modules/@google-cloud/profiler/build/src/index.js:120:15)
    at processTicksAndRejections (node:internal/process/task_queues:96:5)
    at async createProfiler (/usr/src/app/node_modules/@google-cloud/profiler/build/src/index.js:158:26)
    at async Object.start (/usr/src/app/node_modules/@google-cloud/profiler/build/src/index.js:182:22)

To Reproduce

px demo deploy px-online-boutique

Expected behavior
All pods should be in a Running state.
Screenshots
If applicable, add screenshots to help explain your problem. Please make sure the screenshot does not contain any sensitive information such as API keys or access tokens.
Logs
Attached.
App information (please complete the following information):

Pixie version: pem_image:0.12.10
K8s cluster version: v1.22.16-eks-ffeb93d
Node Kernel version: 5.4.226-129.415.amzn2.x86_64
Browser version: N/A

Additional context
Add any other context about the problem here.

It looks like this bug was fixed in https://github.com/GoogleCloudPlatform/microservices-demo/pull/665. We should update our microservice-demo fork and see if this fixes things on EKS.

The workaround is to uncomment all of the DISABLE_* env vars in https://github.com/pixie-io/pixie/blob/main/demos/online-boutique/online-boutique.yaml, then apply the file manually:
# Delete existing demo app
px demo delete px-online-boutique

# Manually deploy modified demo yaml
kubectl create ns px-online-boutique
kubectl apply -f online-boutique.yaml -n px-online-boutique


Python uses signed integer to address cms table

Description
The underlying C code uses unsigned 32-bit integers for the width and depth indices of the count min sketch table.  However, Python is using signed 32-bit integers. This means that if you create a 16GB count min sketch with the default depth = 8 and log_counting = 8, then the width should be 16B / 8 / 1 = 2^32.  But doing so causes an OverflowError.
Steps/Code/Corpus to Reproduce
from bounter import bounter
b = bounter(size_mb=16384, need_iteration=False, log_counting=8)

Expected Results
Expect to create the largest bounter possible without error.
Actual Results
The following error occurs.
bounter.py in bounter(size_mb, need_iteration, need_counts, log_counting)
     37         return HashTable(size_mb=size_mb)
     38     else:
---> 39         return CountMinSketch(size_mb=size_mb, log_counting=log_counting)
count_min_sketch.py in __init__(self, size_mb, width, depth, log_counting)
     99
    100         if log_counting == 8:
--> 101             self.cms = cmsc.CMS_Log8(width=self.width, depth=self.depth)
    102         elif log_counting == 1024:
    103             self.cms = cmsc.CMS_Log1024(width=self.width, depth=self.depth)
OverflowError: signed integer is greater than maximum

Possible Fix
Looking at cbounter/cms_common.c the problem seems to be from Line 65 in the PyArg_ParseTupleAndKeywords(args, kwds, 'ii', kwlist, &w, &self->depth). The 'ii' tells python to use a signed integer for &w and &self->depth.  Switching the lowercase 'ii' to uppercase 'II' would tell python to use an unsigned integer.
This formatting of 'ii' also appears in Line 381 Py_BuildValue("(ii)", self->width, self->depth).
Versions
Linux-3.10.0-693.21.1.el7.x86_64-x86_64-with-centos-7.4.1708-Core
Python 3.6.5 |Anaconda, Inc.| (default, Mar 29 2018, 18:21:58)
[GCC 7.2.0]
NumPy 1.14.2
bounter 1.0.1

@tjbookreader it sounds like you have a good idea of where the bug is and how to fix it. Can you open a PR with the fix?

I'm very new to GitHub.  I believe that I have created the PR.  Let me know if that worked.  Thanks.


labelme2yolo image changed to black

labelme2yolo png for some images changed to black , is there something wrong with scrip.

I got the answer, we should convert image to "RGB"


Error when downloading Windows SDK 10.0.19041.0

Type: Bug
DESCRIPTION: Getting an error when attempting to install a Windows Software Development Kit 10.0.19041.0
REPRO STEPS:

Download any Windows SDK file (I used Windows SDK 10.0.19041.0)
Follow the default choices and attempt to install.
RESULT:
“An error occurred while installing “Kits Configuration Installer”
“Unable to verify the integrity of downloaded content. It might be corrupted. Please check your network connection and try again.”
NOTE:
The bar loads to 14% and sits at “Updating settings” for a couple of minutes and then i am met with the message above.
I have tried restarting my PC already and tried using the most updated SDK available as well.

VS Code version: Code 1.73.0 (8fa188b2b301d36553cbc9ce1b0a146ccb93351f, 2022-11-01T15:34:06.111Z)
OS version: Windows_NT x64 10.0.19044
Modes: Restricted
Sandboxed: No

System Info

Item
Value

CPUs
AMD Ryzen Threadripper PRO 3975WX 32-Cores      (64 x 3493)

GPU Status
2d_canvas: enabledcanvas_oop_rasterization: disabled_offdirect_rendering_display_compositor: disabled_off_okgpu_compositing: enabledmultiple_raster_threads: enabled_onopengl: enabled_onrasterization: enabledraw_draw: disabled_off_okskia_renderer: enabled_onvideo_decode: enabledvideo_encode: enabledvulkan: disabled_offwebgl: enabledwebgl2: enabledwebgpu: disabled_off

Load (avg)
undefined

Memory (System)
127.85GB (101.39GB free)

Process Argv
--crash-reporter-id d93ad162-3505-4bed-b1c7-ffb5a52d3287

Screen Reader
no

VM
0%

Exten
[Windows_Software_Development_Kit___Windows_10.0.19041.685_20221107133917.log](https://github.com/microsoft/vscode/files/9954782/Windows_Software_Development_Kit___Windows_10.0.19041.685_20221107133917.log)
sions: none
A/B Experiments
![IMAGE_WindowsError](https://user-images.githubusercontent.com/117680811/200396353-4f631c06-7556-4b5b-9c30-364c6a6e2768.JPG)
vsliv368:30146709
vsreu685:30147344
python383:30185418
vspor879:30202332
vspor708:30202333
vspor363:30204092
vstes627:30244334
vslsvsres303:30308271
pythonvspyl392:30443607
vserr242cf:30382550
pythontb:30283811
vsjup518:30340749
pythonptprofiler:30281270
vshan820:30294714
vstes263:30335439
vscorecescf:30445987
pythondataviewer:30285071
vscod805:30301674
binariesv615:30325510
bridge0708:30335490
bridge0723:30353136
cmake_vspar411:30581797
vsaa593:30376534
pythonvs932:30410667
cppdebug:30492333
vscaat:30438848
vsclangdc:30486549
c4g48928:30535728
dsvsc012cf:30540253
azure-dev_surveyonecf:30548226
pyindex848cf:30577861
nodejswelcome1cf:30587006
fc301958:30595537
2e4cg342:30602488
iaj6b796:30604794
dbltrim-noruby:30604474

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Import dock randomly causes sidebar size jumps

Godot version
e9e9e92
System information
Windows 10 x64
Issue description
Seems like minimum size of the import dock varies greatly and it can abruptly change sidebar's width. It always happens when switching to Import tab, but also randomly on it's own.

Might be caused #59303
But the random jumps and minsize weirdness looks similar to #43749
Steps to reproduce

Select different files in the filesystem dock (scene, texture, sample etc)
The left dock will randomly change size
If not, switch to Import tab and observe size jump

Minimal reproduction project
No response

Ok I found a way to trigger this reliably. The random jump seems to happen on filesystem update.

So it's indeed the same as #43749 + some random size refresh from filesystem + #59303 making the import dock wider.

It feels like the TabContainer here has use_hidden_tabs_for_min_size enabled

Take back the previous conclusions. drag_ofs uses the split_offset at the start of dragging in the following code, which will cause the split_offset calculated by the change of the minimum size of the child control to be overwritten.
https://github.com/godotengine/godot/blob/4808d01b2bcda54db15e1e2649e0a38c37886ee1/scene/gui/split_container.cpp#L267-L273

This is the culprit for wide import dock:

It either should be shorter or the OptionButton should have trimming enabled


Enable branch policies for HLD repos

Verify and update branch policies for HLD repos using GitHub API. This should be automated by making changes made to the build.sh. Related to #70

If the goal is to require a PR that is approved by at least one approver to make changes to HLD repo, then I believe we can implement something similar to what we have done for Rings:
https://github.com/microsoft/bedrock-rings/blob/1532c9bd02f9b71354894481bf0008aba73b5744/rings-release.sh#L82
Thoughts?
@andrebriggs @samiyaakhtar

Was the original goal of this work item to implement a script that enforces no direct push to master, and a minimum number of approving code reviews? The script @yradsmikham has linked can help us add a PR gate to the workflow, but it might remove the automation we have in place (of having things end up in cluster when a source commit is made).


RadioGroup's Clone implementation is unnecessarily bounded

Problem
RadioGroup implements Clone trough a derive, this derive introduces a T: Clone bound on the implementation.
The only place T is stored, is behind an Rc; this Rc is perfectly cloneable for non-cloneable T, so there is no reason for the aforementioned bound.
Solution
Implement Clone manually, as in
impl<T: 'static> Clone for RadioGroup<T> {
    fn clone(&self) -> Self {
        Self{
            state: self.state.clone()
        }
    }
}

Ah indeed, that's one of the issues with derived traits.

Deployed with cursive 0.16.0.

Deployed with cursive 0.16.0.


TSDB: lock-free total counter

Reduce locking by using atomic

Hi @dgl
could you please take a quick look into this?