Any help with a trace rename error? Thoughts?

line 209 in bin.js
Error:
Error: ENOENT: no such file or directory, rename 'node_trace.1.log' -> '46525.clinic-doctor/46525.clinic-doctor-traceevent'
    at Error (native)

+1 I get the same error

What node version are you using? Until node 8.10.0 is release, we recommend you use 9.4.0.

Same error with node v6.5.0

I'm back on 6.11. I'll give 9.4 a try. Thanks!

I'm getting the same thing on Node 9.4.0 (+ macOS 10.13.3, clinic v0.6.2)
/Users/jiripospisil/.nah/versions/release--v9.4.0/lib/node_modules/clinic/bin.js:209
      if (err) throw err
               ^

Error: ENOENT: no such file or directory, rename 'node_trace.1.log' -> '40419.clinic-doctor/40419.clinic-doctor-traceevent'

Ah, it's because my app catches SIGINT (which I guess should trigger the dump of the trace) and does some clean up. If I remove the signal catching, it works fine.

@jiripospisil That sounds odd. We collect the data continually and flush the final data on process exit. The flushing part is a bit complicated when the process doesn't exit nicely, and works best when the process exits naturally. Thus I would actually recommend you to add a SIGINT handler that closes everything nicely. See for example our slow-io example: https://github.com/nearform/node-clinic-examples/blob/master/slow-io/index.js#L29
PS: We have already made a few improvement to node.js itself, such it works better when there is no SIGINT handler. However, it is an ongoing effort.

@AndreasMadsen  Hmm, so the difference seems to be that I use process.once instead of process.on. If works fine if leave the example as is.
/tmp/tp-20180203201937/node-clinic-examples/slow-io ∴ master gd
diff --git a/slow-io/index.js b/slow-io/index.js
index 079d5dc..26ba90e 100644
--- a/slow-io/index.js
+++ b/slow-io/index.js
@@ -26,7 +26,7 @@ server.get('/', function (req, res, next) {

 server.listen(3000)

-process.on('SIGINT', function () {
+process.once('SIGINT', function () {
   console.error('Caught SIGINT, shutting down.')
   server.close()
 })
/tmp/tp-20180203201937/node-clinic-examples/slow-io ∴ master clinic doctor -- node index.js
Warning: Trace event is an experimental feature and could change at any time.
^C/Users/jiripospisil/.nah/versions/release--v9.4.0/lib/node_modules/clinic/bin.js:209
      if (err) throw err
               ^

Error: ENOENT: no such file or directory, rename 'node_trace.1.log' -> '43741.clinic-doctor/43741.clinic-doctor-traceevent'

@jiripospisil Thanks, I can reproduce the issue – it is quite odd. I will have a look on Monday.

Any updates on this? Anything I can do to help?

I traced it down to this bug https://github.com/nodejs/node/issues/18600 – We have a workaround for another nodecore bug, that as a side-effect will also workaround this nodecore bug: https://github.com/nearform/node-clinic-doctor/pull/100
I will push for it getting landed soon :)

We published a new version of @nearform/clinic-doctor, if you update clinic it should work now :)

I think this has been fixed. If not please reopen :)

Getting the same error today! Any help?

Getting the same error on Node v6 today! Any help?

Node 6 is not supported. Use Node 8.10z

How can I change the directory where the node_trace.*.log should go


Sample size /Power calculations	- Group-sequential designs

Is there an interest to make a comparison of R vs EAST? If so, I am interested to contribute on that topic

Hello @yuliasidi, we would love to have comparisons with R and EAST. It would be really good

@statasaurus, the initial examples for R and EAST were added in PR https://github.com/PSIAIMS/CAMIS/pull/231
Still working on the comparison between the two.

@yuliasidi Thanks for adding the EAST & R content.  Could you write us a short comparison saying whether the methods you use in both software matched?   You could either update the EAST one to say "As you can see results match the R page" and link to that... or if there are discrepancies, add a new page "R vs East" discussion the differences.

Sorry for taking so long. PR for this was finally submitted
https://github.com/PSIAIMS/CAMIS/pull/356#issue-2691891187

Thank you so much @yuliasidi , we will pull it in this week hopefully


Message prefix/suffix

Ability to add prefix/suffix instruction to first message in chat. Best as .env variable. I want to instruct openAI about policies. Kindly give me a pointer so I  can attempt this.

I added default message for system-role, but I think prefix/suffix would make my task easier

You can add prefixes and suffixes directly in llms.py, but you can also add GLOBAL_PREFIX, GLOBAL_SUFFIX to your .env. Setting the GLOBAL value will override the prefix and suffix set in LLM.
f67896718580c460514a80aa23f74cb60df5af49


MTDSA-817 - Investigate why Income Store expects us to pass Correlati…

…onId back to the 3PP.

👍


Add support to assign administration access based on LDAP group / acl

In #1305 and #3115 requests were made to be able to assign project level permissions based on LDAP groups. It seems like this is currently being worked on in #3506. However, I was surprised to see that no one had requested the ability to assign harbor administrator access based on an LDAP group / acl.
For instance, in harbor.cfg there could be a setting "ldap_admin_acl". When a user with that acl logs in through the web UI, they would get access to the "Administration" tab on the left side and all of the privileges that come along with it.
Thanks

@stonezdj please dup.

duplicated with #4828, close


DFBUGS-922: [release-4.15] Revert: Reverting CVE fix for DFBUGS-922 and DFBUGS-934

Fixes: https://issues.redhat.com/browse/DFBUGS-922
Fixes: https://issues.redhat.com/browse/DFBUGS-934

/jira refresh

/approve
/lgtm


Adding BLS to OptunaRCG

Adds support for BLS models to the OptunaRCG.
Here is an example BLS configuration I ran live:
run_config_search_mode: optuna
use_concurrency_formula: True
profile_models: bls
bls_composing_models:
  add:
    model_config_parameters:
      dynamic_batching:
        max_queue_delay_microseconds: [100, 200, 300]
  sub:
    model_config_parameters:
      dynamic_batching:
        max_queue_delay_microseconds: [400, 500, 600]

And here's some of the output log:
15:14:08 [Model Analyzer] Starting Optuna mode search to find optimal configs
15:14:08 [Model Analyzer]
[I 2024-06-13 15:14:08,236] A new study created in memory with name: bls
15:14:08 [Model Analyzer] Measuring default configuration to establish a baseline measurement
15:14:08 [Model Analyzer] Creating model config: bls_config_default
15:14:08 [Model Analyzer] Creating model config: add_config_default
15:14:08 [Model Analyzer] Creating model config: sub_config_default
15:14:08 [Model Analyzer]
15:14:08 [Model Analyzer] DEBUG: Triton Server started.
15:14:11 [Model Analyzer] DEBUG: Model add_config_default loaded
15:14:14 [Model Analyzer] DEBUG: Model sub_config_default loaded
15:14:17 [Model Analyzer] DEBUG: Model bls_config_default loaded
15:14:17 [Model Analyzer] Profiling bls_config_default: concurrency=0
15:14:23 [Model Analyzer]
15:14:23 [Model Analyzer] DEBUG: Number of configs in search space: 1125
15:14:23 [Model Analyzer] DEBUG: Model - bls:
15:14:23 [Model Analyzer] DEBUG:   instance_group: 1 to 5 (5)
15:14:23 [Model Analyzer] DEBUG: Composing model - add:
15:14:23 [Model Analyzer] DEBUG:   instance_group: 1 to 5 (5)
15:14:23 [Model Analyzer] DEBUG:   max_queue_delay_microseconds: [100, 200, 300] (3)
15:14:23 [Model Analyzer] DEBUG: Composing model - sub:
15:14:23 [Model Analyzer] DEBUG:   instance_group: 1 to 5 (5)
15:14:23 [Model Analyzer] DEBUG:   max_queue_delay_microseconds: [400, 500, 600] (3)
15:14:23 [Model Analyzer]
15:14:23 [Model Analyzer] DEBUG: Minimum number of trials: 56 (5% of search space)
15:14:23 [Model Analyzer] DEBUG: Maximum number of trials: 112 (10% of search space)
15:14:23 [Model Analyzer]
15:14:23 [Model Analyzer] DEBUG: Trial 1 of 112:
15:14:23 [Model Analyzer] Creating model config: bls_config_0
15:14:23 [Model Analyzer]   Setting instance_group to [{'count': 2, 'kind': 'KIND_GPU'}]
15:14:23 [Model Analyzer]
15:14:23 [Model Analyzer] Creating model config: add_config_0
15:14:23 [Model Analyzer]   Setting instance_group to [{'count': 2, 'kind': 'KIND_GPU'}]
15:14:23 [Model Analyzer]   Setting dynamic_batching to {'max_queue_delay_microseconds': 100}
15:14:23 [Model Analyzer]
15:14:23 [Model Analyzer] Creating model config: sub_config_0
15:14:23 [Model Analyzer]   Setting instance_group to [{'count': 4, 'kind': 'KIND_GPU'}]
15:14:23 [Model Analyzer]   Setting dynamic_batching to {'max_queue_delay_microseconds': 500}
15:14:23 [Model Analyzer]
15:14:37 [Model Analyzer] Profiling bls_config_0: concurrency=4
15:14:41 [Model Analyzer] DEBUG: Objective score for bls_config_0::add_config_0,sub_config_0: 54 --- Best: bls_config_0::add_config_0,sub_config_0 (54)
...
15:16:10 [Model Analyzer] DEBUG: Trial 7 of 112:
15:16:10 [Model Analyzer] Found existing model config: bls_config_1
15:16:10 [Model Analyzer]   Setting instance_group to [{'count': 3, 'kind': 'KIND_GPU'}]
15:16:10 [Model Analyzer]
15:16:10 [Model Analyzer] Creating model config: add_config_5
15:16:10 [Model Analyzer]   Setting instance_group to [{'count': 4, 'kind': 'KIND_GPU'}]
15:16:10 [Model Analyzer]   Setting dynamic_batching to {'max_queue_delay_microseconds': 300}
15:16:10 [Model Analyzer]
15:16:10 [Model Analyzer] Found existing model config: sub_config_3
15:16:10 [Model Analyzer]   Setting instance_group to [{'count': 1, 'kind': 'KIND_GPU'}]
15:16:10 [Model Analyzer]   Setting dynamic_batching to {'max_queue_delay_microseconds': 600}
15:16:10 [Model Analyzer]
15:16:25 [Model Analyzer] Profiling bls_config_1: concurrency=6
15:16:29 [Model Analyzer] DEBUG: Objective score for bls_config_1::add_config_5,sub_config_3: 83 --- Best: bls_config_2::add_config_2,sub_config_2 (153)
...
15:31:04 [Model Analyzer] DEBUG: Trial 56 of 112:
15:31:04 [Model Analyzer] Found existing model config: bls_config_4
15:31:04 [Model Analyzer]   Setting instance_group to [{'count': 4, 'kind': 'KIND_GPU'}]
15:31:04 [Model Analyzer]
15:31:04 [Model Analyzer] Found existing model config: add_config_2
15:31:04 [Model Analyzer]   Setting instance_group to [{'count': 4, 'kind': 'KIND_GPU'}]
15:31:04 [Model Analyzer]   Setting dynamic_batching to {'max_queue_delay_microseconds': 100}
15:31:04 [Model Analyzer]
15:31:04 [Model Analyzer] Found existing model config: sub_config_6
15:31:04 [Model Analyzer]   Setting instance_group to [{'count': 2, 'kind': 'KIND_GPU'}]
15:31:04 [Model Analyzer]   Setting dynamic_batching to {'max_queue_delay_microseconds': 400}
15:31:04 [Model Analyzer]
15:31:24 [Model Analyzer] DEBUG: Objective score for bls_config_4::add_config_2,sub_config_6: 102 --- Best: bls_config_2::add_config_11,sub_config_6 (158)
15:31:24 [Model Analyzer]
15:31:24 [Model Analyzer] DEBUG: Early termination threshold reached
15:31:24 [Model Analyzer]
15:31:24 [Model Analyzer] Done with Optuna mode search. Gathering concurrency sweep measurements for reports
...

Does that model not have batching? The report you printed at the end mentions max batch size of 0

Does that model not have batching? The report you printed at the end mentions max batch size of 0

Correct.  This model does not support batching.


Extend certificate fingerprinting support.

While urllib3 has certificate fingerprinting support, it would be extremely powerful to have more fine-grained fingerprinting support. In particular, the current support is very binary: either you fingerprint or you don't. If you want to fingerprint on only some domains you need to abandon the PoolManager entirely, because you need to set assert_fingerprint on the ConnectionPool object before you call urlopen.
It would be very nice if it was possible to do fingerprinting on a more granular level. Some options:

pass assert_fingerprint a function that returns a fingerprint for a given hostname.
pass assert_fingerprint a dict of hostnames to fingerprints

In all cases we'd fall back to asserting hostname or other if the user does not provide a fingerprint for a given hostname.
This would allow users to fingerprint where they can or choose to, while not preventing them from making less-secure HTTPS calls.
Thoughts?

---
Want to back this issue? **[Post a bounty on it!](https://www.bountysource.com/issues/9166965-extend-certificate-fingerprinting-support?utm_campaign=plugin&utm_content=tracker%2F192525&utm_medium=issues&utm_source=github)** We accept bounties via [Bountysource](https://www.bountysource.com/?utm_campaign=plugin&utm_content=tracker%2F192525&utm_medium=issues&utm_source=github).

Related: #257
I really like this idea of being able to build custom verification schemes without too much trouble, but we couldn't agree on the API design and the PR stagnated.


Error doing  composer run.py --config-path yamls/hydra-yamls --config-name SD-2-base-512.yaml

composer run.py --config-path yamls/hydra-yamls --config-name SD-2-base-512.yaml
[2023-06-13 20:29:52,077][composer.utils.reproducibility][INFO] - Setting seed to 17
Error executing job with overrides: []
Error in call to target 'diffusion.models.models.stable_diffusion_2':
TypeError("UNet2DConditionModel.init() got an unexpected keyword argument 'dual_cross_attention'")
full_key: model
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
ERROR:composer.cli.launcher:Rank 3 crashed with exit code 1.
Waiting up to 30 seconds for all training processes to terminate. Press Ctrl-C to exit immediately.
Global rank 3 (PID 40553) exited with code 1
----------Begin global rank 3 STDOUT----------
[2023-06-13 20:29:52,032][composer.utils.reproducibility][INFO] - Setting seed to 17
----------End global rank 3 STDOUT----------
----------Begin global rank 3 STDERR----------
Error executing job with overrides: []
Error in call to target 'diffusion.models.models.stable_diffusion_2':
TypeError("UNet2DConditionModel.init() got an unexpected keyword argument 'dual_cross_attention'")
full_key: model
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
----------End global rank 3 STDERR----------
ERROR:composer.cli.launcher:Global rank 0 (PID 40550) exited with code -15

Can you provide the output of pip list from the machine you are trying to run this on?

Set XFORMERS_MORE_DETAILS=1 for more details
ERROR:composer.cli.launcher:Rank 6 crashed with exit code 1.
Waiting up to 30 seconds for all training processes to terminate. Press Ctrl-C to exit immediately.
Global rank 0 (PID 1594036) exited with code 143
Global rank 1 (PID 1594037) exited with code 143
----------Begin global rank 1 STDOUT----------
[2023-06-21 16:23:25,799][composer.utils.reproducibility][INFO] - Setting seed to 17
[2023-06-21 16:23:25,863][xformers][WARNING] - WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:
PyTorch 1.13.1+cu117 with CUDA 1107 (you have 1.13.1)
Python  3.9.16 (you have 3.9.16)
Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)
Memory-efficient attention, SwiGLU, sparse and more won't be available.
Set XFORMERS_MORE_DETAILS=1 for more details
----------End global rank 1 STDOUT----------
----------Begin global rank 1 STDERR----------
/root/envs/py39_dl/lib/python3.9/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: libtorch_cuda_cu.so: cannot open shared object file: No such file or directory
warn(f"Failed to load image Python extension: {e}")
----------End global rank 1 STDERR----------
Global rank 2 (PID 1594038) exited with code 143
----------Begin global rank 2 STDOUT----------
[2023-06-21 16:23:26,238][composer.utils.reproducibility][INFO] - Setting seed to 17
[2023-06-21 16:23:26,295][xformers][WARNING] - WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:
PyTorch 1.13.1+cu117 with CUDA 1107 (you have 1.13.1)
Python  3.9.16 (you have 3.9.16)
Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)
Memory-efficient attention, SwiGLU, sparse and more won't be available.
Set XFORMERS_MORE_DETAILS=1 for more details
----------End global rank 2 STDOUT----------
----------Begin global rank 2 STDERR----------
/root/envs/py39_dl/lib/python3.9/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: libtorch_cuda_cu.so: cannot open shared object file: No such file or directory
warn(f"Failed to load image Python extension: {e}")
----------End global rank 2 STDERR----------
Global rank 3 (PID 1594039) exited with code 143
----------Begin global rank 3 STDOUT----------
[2023-06-21 16:23:26,143][composer.utils.reproducibility][INFO] - Setting seed to 17
[2023-06-21 16:23:26,194][xformers][WARNING] - WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:
PyTorch 1.13.1+cu117 with CUDA 1107 (you have 1.13.1)
Python  3.9.16 (you have 3.9.16)
Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)
Memory-efficient attention, SwiGLU, sparse and more won't be available.
Set XFORMERS_MORE_DETAILS=1 for more details
----------End global rank 3 STDOUT----------
----------Begin global rank 3 STDERR----------
/root/envs/py39_dl/lib/python3.9/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: libtorch_cuda_cu.so: cannot open shared object file: No such file or directory
warn(f"Failed to load image Python extension: {e}")
----------End global rank 3 STDERR----------
Global rank 4 (PID 1594040) exited with code 143
----------Begin global rank 4 STDOUT----------
[2023-06-21 16:23:26,017][composer.utils.reproducibility][INFO] - Setting seed to 17
[2023-06-21 16:23:26,068][xformers][WARNING] - WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:
PyTorch 1.13.1+cu117 with CUDA 1107 (you have 1.13.1)
Python  3.9.16 (you have 3.9.16)
Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)
Memory-efficient attention, SwiGLU, sparse and more won't be available.
Set XFORMERS_MORE_DETAILS=1 for more details
----------End global rank 4 STDOUT----------
----------Begin global rank 4 STDERR----------
/root/envs/py39_dl/lib/python3.9/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: libtorch_cuda_cu.so: cannot open shared object file: No such file or directory
warn(f"Failed to load image Python extension: {e}")
----------End global rank 4 STDERR----------
Global rank 5 (PID 1594041) exited with code 143
----------Begin global rank 5 STDOUT----------
[2023-06-21 16:23:25,865][composer.utils.reproducibility][INFO] - Setting seed to 17
[2023-06-21 16:23:25,922][xformers][WARNING] - WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:
PyTorch 1.13.1+cu117 with CUDA 1107 (you have 1.13.1)
Python  3.9.16 (you have 3.9.16)
Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)
Memory-efficient attention, SwiGLU, sparse and more won't be available.
Set XFORMERS_MORE_DETAILS=1 for more details
----------End global rank 5 STDOUT----------
----------Begin global rank 5 STDERR----------
/root/envs/py39_dl/lib/python3.9/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: libtorch_cuda_cu.so: cannot open shared object file: No such file or directory
warn(f"Failed to load image Python extension: {e}")
----------End global rank 5 STDERR----------
Global rank 6 (PID 1594042) exited with code 1
----------Begin global rank 6 STDOUT----------
[2023-06-21 16:23:25,761][composer.utils.reproducibility][INFO] - Setting seed to 17
[2023-06-21 16:23:25,863][xformers][WARNING] - WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:
PyTorch 1.13.1+cu117 with CUDA 1107 (you have 1.13.1)
Python  3.9.16 (you have 3.9.16)
Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)
Memory-efficient attention, SwiGLU, sparse and more won't be available.
Set XFORMERS_MORE_DETAILS=1 for more details
----------End global rank 6 STDOUT----------
----------Begin global rank 6 STDERR----------
/root/envs/py39_dl/lib/python3.9/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: libtorch_cuda_cu.so: cannot open shared object file: No such file or directory
warn(f"Failed to load image Python extension: {e}")
Error executing job with overrides: []
Error in call to target 'diffusion.models.models.stable_diffusion_2':
RuntimeError('CUDA error: invalid device ordinal\nCUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1.')
full_key: model
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
----------End global rank 6 STDERR----------
Global rank 7 (PID 1594043) exited with code 143
----------Begin global rank 7 STDOUT----------
[2023-06-21 16:23:25,912][composer.utils.reproducibility][INFO] - Setting seed to 17
[2023-06-21 16:23:25,994][xformers][WARNING] - WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:
PyTorch 1.13.1+cu117 with CUDA 1107 (you have 1.13.1)
Python  3.9.16 (you have 3.9.16)
Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)
Memory-efficient attention, SwiGLU, sparse and more won't be available.
Set XFORMERS_MORE_DETAILS=1 for more details
----------End global rank 7 STDOUT----------
----------Begin global rank 7 STDERR----------
/root/envs/py39_dl/lib/python3.9/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: libtorch_cuda_cu.so: cannot open shared object file: No such file or directory
warn(f"Failed to load image Python extension: {e}")
----------End global rank 7 STDERR----------
ERROR:composer.cli.launcher:Global rank 0 (PID 1594036) exited with code 143

Hello, apologies for the delay. This seems like a setup, but I can't pinpoint exactly what is going wrong.
A few questions:

What changes did you make to the SD-2-base-512.yaml file?
Could you try running this with diffusers 0.16? This is the version we used for our training run and recently pinned this version in our setup.py
The second issue seems to be related to your CUDA install, could you provide the output to to nvidia-smi?


Question: Presence Detection

Greetings from Colombia.
Not a developer, but handy enough with scripting and linux to somehow get BLE capture from TI CC2540 and NRF24.
Wanted to ask you if you would have any suggestions on how to track the RSSI of a Smartwatch (garmin) that is already paired to a Smartphone (Smartwatch does not broadcast itself once is paired).
Thanks!

Hi, sibero80
If it is paired, using the RSSI does not work.


Cuda error using environment

2024-09-30 14:31:52.339775: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/software/slurm/21.08.5/lib
2024-09-30 14:31:52.343602: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.

"\alma-fs\SCRATCH\DMP\UCEC\GENEVOD\ntrahearn\ClassifierTest\logsD\d_run_0.out"

Resolved by back tracking the mamba init on Nick's session.


Fix not showing link to some checker results

Previously we adopted this hack in favour of manually specifying a
fake "description" for each subscriber list 1. However, we forgot
that the URL for checker results has changed. This fixes the hack
to cope with both versions of the checker results URL.

Tested and works in Staging ✅.


Device Request - [Moes zigbee smart IR remote] - [_TZ3290_ot6ewjvmejq5ekhl] / [TS1201]

Prerequisites:

Before requesting a device addition, please ensure there is not already a request for the device among the open issues.
Make sure your Homey is upgraded to firmware v5 or higher.
You need a physical example of the device

Device Name: [Moes zigbee smart IR remote]
Device Model: [TS1201]
Device Description: [Zigbee IR remote]
Link to device image: [https://shop.smarthome-europe.com/14899-thickbox_default/moes-tuya-zigbee-infrared-universal-remote-control.jpg]
Device Interview:
"ids": {
"modelId": "TS1201",
"manufacturerName": "_TZ3290_ot6ewjvmejq5ekhl"
},
"endpoints": {
"ieeeAddress": "f0:82:c0:ff:fe:fd:e7:46",
"networkAddress": 19325,
"modelId": "TS1201",
"manufacturerName": "_TZ3290_ot6ewjvmejq5ekhl",
"endpointDescriptors": [
{
"status": "SUCCESS",
"nwkAddrOfInterest": 19325,
"_reserved": 28,
"endpointId": 1,
"applicationProfileId": 260,
"applicationDeviceId": 61440,
"applicationDeviceVersion": 0,
"_reserved1": 1,
"inputClusters": [
0,
4,
5,
3,
1,
60672,
57348,
6
],
"outputClusters": [
25,
10
]
}
],
"deviceType": "enddevice",
"receiveWhenIdle": false,
"capabilities": {
"alternatePANCoordinator": false,
"deviceType": false,
"powerSourceMains": false,
"receiveWhenIdle": false,
"security": false,
"allocateAddress": true
},
"extendedEndpointDescriptors": {
"1": {
"clusters": {
"basic": {
"attributes": [
{
"acl": [
"readable",
"reportable"
],
"id": 0,
"name": "zclVersion",
"value": 3,
"reportingConfiguration": {
"status": "NOT_FOUND",
"direction": "reported"
}
},
{
"acl": [
"readable",
"reportable"
],
"id": 1,
"name": "appVersion",
"value": 67,
"reportingConfiguration": {
"direction": "reported",
"attributeDataType": 32,
"minInterval": 1,
"maxInterval": 150,
"minChange": 0,
"status": "SUCCESS"
}
},
{
"acl": [
"readable",
"reportable"
],
"id": 2,
"name": "stackVersion",
"value": 0,
"reportingConfiguration": {
"status": "NOT_FOUND",
"direction": "reported"
}
},
{
"acl": [
"readable",
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],
"id": 3,
"name": "hwVersion",
"value": 1,
"reportingConfiguration": {
"status": "NOT_FOUND",
"direction": "reported"
}
},
{
"acl": [
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],
"id": 4,
"name": "manufacturerName",
"value": "_TZ3290_ot6ewjvmejq5ekhl",
"reportingConfiguration": {
"status": "NOT_FOUND",
"direction": "reported"
}
},
{
"acl": [
"readable",
"reportable"
],
"id": 5,
"name": "modelId",
"value": "TS1201",
"reportingConfiguration": {
"status": "NOT_FOUND",
"direction": "reported"
}
},
{
"acl": [
"readable",
"reportable"
],
"id": 6,
"name": "dateCode",
"value": "",
"reportingConfiguration": {
"status": "NOT_FOUND",
"direction": "reported"
}
},
{
"acl": [
"readable",
"reportable"
],
"id": 7,
"name": "powerSource",
"value": "battery",
"reportingConfiguration": {
"status": "NOT_FOUND",
"direction": "reported"
}
},
{
"acl": [
"readable",
"writable",
"reportable"
],
"id": 65502,
"reportingConfiguration": {
"status": "NOT_FOUND",
"direction": "reported"
}
},
{
"acl": [
"readable",
"reportable"
],
"id": 65533,
"name": "clusterRevision",
"value": 2,
"reportingConfiguration": {
"status": "NOT_FOUND",
"direction": "reported"
}
},
{
"acl": [
"readable",
"reportable"
],
"id": 65534,
"name": "attributeReportingStatus",
"value": "PENDING",
"reportingConfiguration": {
"status": "NOT_FOUND",
"direction": "reported"
}
},
{
"acl": [
"readable",
"reportable"
],
"id": 65504,
"reportingConfiguration": {
"status": "NOT_FOUND",
"direction": "reported"
}
},
{
"acl": [
"readable",
"reportable"
],
"id": 65505,
"reportingConfiguration": {
"status": "NOT_FOUND",
"direction": "reported"
}
},
{
"acl": [
"readable",
"reportable"
],
"id": 65506,
"reportingConfiguration": {
"status": "NOT_FOUND",
"direction": "reported"
}
},
{
"acl": [
"readable",
"reportable"
],
"id": 65507,
"reportingConfiguration": {
"status": "NOT_FOUND",
"direction": "reported"
}
}
],
"commandsGenerated": "UNSUP_GENERAL_COMMAND",
"commandsReceived": "UNSUP_GENERAL_COMMAND"
},
"groups": {
"attributes": [
{
"acl": [
"readable",
"reportable"
],
"id": 0,
"name": "nameSupport",
"value": {
"type": "Buffer",
"data": [
0
]
},
"reportingConfiguration": {
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}
},
{
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"reportingConfiguration": {
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"direction": "reported"
}
}
],
"commandsGenerated": "UNSUP_GENERAL_COMMAND",
"commandsReceived": "UNSUP_GENERAL_COMMAND"
},
"scenes": {
"attributes": [
{
"acl": [
"readable",
"reportable"
],
"id": 0,
"reportingConfiguration": {
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{
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"id": 1,
"reportingConfiguration": {
"status": "NOT_FOUND",
"direction": "reported"
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},
{
"acl": [
"readable",
"reportable"
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"id": 2,
"reportingConfiguration": {
"status": "NOT_FOUND",
"direction": "reported"
}
},
{
"acl": [
"readable",
"reportable"
],
"id": 3,
"reportingConfiguration": {
"status": "NOT_FOUND",
"direction": "reported"
}
},
{
"acl": [
"readable",
"reportable"
],
"id": 4,
"reportingConfiguration": {
"status": "NOT_FOUND",
"direction": "reported"
}
},
{
"acl": [
"readable",
"reportable"
],
"id": 65533,
"name": "clusterRevision",
"value": 2,
"reportingConfiguration": {
"status": "NOT_FOUND",
"direction": "reported"
}
}
],
"commandsGenerated": "UNSUP_GENERAL_COMMAND",
"commandsReceived": "UNSUP_GENERAL_COMMAND"
},
"identify": {
"attributes": [
{
"acl": [
"readable",
"writable",
"reportable"
],
"id": 0,
"name": "identifyTime",
"value": 0,
"reportingConfiguration": {
"status": "NOT_FOUND",
"direction": "reported"
}
},
{
"acl": [
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"reportable"
],
"id": 65533,
"name": "clusterRevision",
"value": 1,
"reportingConfiguration": {
"status": "NOT_FOUND",
"direction": "reported"
}
}
],
"commandsGenerated": "UNSUP_GENERAL_COMMAND",
"commandsReceived": "UNSUP_GENERAL_COMMAND"
},
"powerConfiguration": {
"attributes": [
{
"acl": [
"readable",
"reportable"
],
"id": 0,
"reportingConfiguration": {
"status": "NOT_FOUND",
"direction": "reported"
}
},
{
"acl": [
"readable",
"reportable"
],
"id": 32,
"name": "batteryVoltage",
"value": 15,
"reportingConfiguration": {
"status": "NOT_FOUND",
"direction": "reported"
}
},
{
"acl": [
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"id": 33,
"name": "batteryPercentageRemaining",
"value": 200,
"reportingConfiguration": {
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"direction": "reported"
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},
{
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"id": 65533,
"name": "clusterRevision",
"value": 1,
"reportingConfiguration": {
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}
}
],
"commandsGenerated": "UNSUP_GENERAL_COMMAND",
"commandsReceived": "UNSUP_GENERAL_COMMAND"
},
"onOff": {
"attributes": [
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"id": 0,
"name": "onOff",
"value": false,
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"minInterval": 1,
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"status": "SUCCESS"
}
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{
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"id": 65533,
"name": "clusterRevision",
"value": 2,
"reportingConfiguration": {
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}
}
],
"commandsGenerated": "UNSUP_GENERAL_COMMAND",
"commandsReceived": "UNSUP_GENERAL_COMMAND"
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},
"bindings": {
"ota": {},
"time": {}
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}
}
}
Additional Comments:
Bought on AliExpress
How to interview a device:

Add the device as a generic Zigbee device in Homey
Navigate to https://developer.athom.com/tools/zigbee.
Interview the device, the button for this is to the right of the device in the list of Zigbee units.
Click the copy button/icon to capture the device information.
Paste the copied information above.

Note: To be able to add more devices to the Tuya Zigbee app, we rely on community members like you to provide interviews of the devices you want added. Thank you for your contribution!

https://github.com/zigpy/zha-device-handlers/pull/2336

hi, I'm pretty new to this GitHub, but have the same device at home that is not yet recognized correctly by Homey. I checked the links you posted above; seems to me there is a quirk made for Home Assistant. I do however not understand yet if this means it's fixed for Homey as well, or that you (or someone else) is still working on it. Did i miss something?

I did not want to wait so I bought a homey bridge that was on sale locally for around €50. Connected it to homey and it gives you IR capabilities but also lots of extra's.
The other device is waiting in a box


cannot completely remove vcpkg once installed

I had vcpkg installed in a certain directory.  I've made attempts to remove it from that location (such as running "vcpkg integrate remove").
I then git cloned vcpkg to a new location.  But somehow it still references the old location, which causes problems.  How should vcpkg be completely purged from a Win10 system?
The old directory was c:\src\darknet\.
The new directory is c:\src\vcpkg\.  When I run commands such as this one:
.\vcpkg.exe install opencv[contrib,core,dnn,ffmpeg,jpeg,png,quirc,tiff,webp]:x64-windows darknet[opencv-base]:x64-windows

The error that I get mention the old vcpkg directory:
Starting package 28/28: darknet:x64-windows
Building package darknet[core,opencv-base]:x64-windows...
-- Downloading https://github.com/AlexeyAB/darknet/archive/91efb7c7f6c00a4616ed58735f7c670908d0a849.tar.gz -> AlexeyAB-darknet-91efb7c7f6c00a4616ed58735f7c670908d0a849.tar.gz...
-- Extracting source C:/src/darknet/vcpkg/downloads/AlexeyAB-darknet-91efb7c7f6c00a4616ed58735f7c670908d0a849.tar.gz
-- Using source at C:/src/darknet/vcpkg/buildtrees/darknet/src/0908d0a849-27adeee8f8.clean
-- Found external ninja('1.10.2').
-- Configuring x64-windows-dbg
-- Configuring x64-windows-rel
-- Building x64-windows-dbg
-- Building x64-windows-rel
-- Installing: C:/src/darknet/vcpkg/packages/darknet_x64-windows/tools/darknet/scripts/download_weights.ps1
-- To download weight files, please go to C:/src/darknet/vcpkg/installed/x64-windows/tools/darknet/scripts and run ./download_weights.ps1
-- Installing: C:/src/darknet/vcpkg/packages/darknet_x64-windows/share/darknet/copyright
-- Performing post-build validation
The following EXEs were found in /bin or /debug/bin. EXEs are not valid distribution targets.

    C:/src/darknet/vcpkg/packages/darknet_x64-windows/bin/uselib_track.exe

The following EXEs were found in /bin or /debug/bin. EXEs are not valid distribution targets.

    C:/src/darknet/vcpkg/packages/darknet_x64-windows/debug/bin/uselib_track.exe

Found 2 error(s). Please correct the portfile:
    C:\src\darknet\vcpkg\ports\darknet\portfile.cmake

Is there a command we can run to completely reset vcpkg?  Or files we need to manually delete so it doesn't use the old location anymore?

did you run bootstrap in new directory.

Yes I did.  @cenit tells me it is because I didn't exit from the powershell and start a new one.  Additionally, I think he's also made a change to the darknet package so this won't be an issue anymore.  I plan on going back to this project this weekend, will test things then.


Incorrect/dark functional images after SDC with SyN

What happened?
I am testing version 21.0.2 on an fMRI dataset for which fieldmaps were not acquired. As such, I am using the SyN method to attempt susceptibility distortion correction. However, I am getting strange results, inconsistent across subjects. I provide two examples.

[sub-01] The SDC estimation includes a considerable translation to match the functional and the anatomical images, which results in this estimated fieldmap

and corrected image:

I see no problems in the func to T1w or T1w to MNI registrations.

[sub-04] The estimated fieldmap seems more reasonable

but the corrected image is mostly dark

I have tested fmriPrep on the same dataset without the SyN correction and all seems fine.

Docker
Is your data BIDS valid?
Yes
Are you reusing any previously computed results?
No
Please copy and paste any relevant log output.
No errors to report in either case.

Additional information / screenshots
I share the .svg images also in this Gdrive folder https://drive.google.com/drive/folders/1RcC4568D6_04gqNGFGp-j4Ec6o8_vdPp?usp=sharing

Hi, I just opened this issue (https://github.com/nipreps/fmriprep/issues/2823) and I think our problems may be related. Did you solve this? Do you have any logs that could be interesting?

Are there any subjects in the dataset where the SyN correction worked as expected?

On our side, processing multi-echo scans works without any problems when non multi-echo scans that were being processed successfully in version 20.0.2 breaks.

Are there any subjects in the dataset where the SyN correction worked as expected?

Hi, yes, for some subjects it works fine.

Good to hear - the --use-syn-sdc flag is still fairly experimental, so I can't say I'm surprised about these discrepancies. Do you know if there is something different (scanning parameters, missing metadata, etc) about the failing participants?
If you have any data that you can share with us (T1w + BOLD) of one of the failing runs, it will help us tremendously to improve the robustness of the SyN implementation.

Hi, I tried the "Syn" procedure for the first time with fmriprep v21.0.2 and couldn't make it work for the 3 subjects I tested.

For these subjects we have the fmap images available for the pepolar SDC method to be used. When we use the pepolar method, fmriprep worked as expected.
To compare with the "Syn" method, I relaunched the same execution (renaming the output and working directories) by adding --ignore filedmaps --use-syn-sdc but even if in the log output I could read:

	 Option "--ignore fieldmaps" was set, but either "--use-syn-sdc" or "--force-syn" were given, so fieldmap-less estimation will be executed.

in the end no SDC method was used for this execution.

I moved away the fmap folder, as if we didn't have any fieldmap acquisition for these subjects and launched again (renaming again the working and output directories) the fmriprep execution with "--use-syn-sdc" . This time the "Syn" SDC procedure was indeed launched for fmriprep crashed for the 3 participants.

Here is output logs for the failed execution using "Syn" .
output.txt

A test on another dataset with the same command (with other acquisition parameters and less distorsion than the one I presented above) with "Syn" option didn't fail with fmriprep v21.0.2:

I'm seeing a very similar result to https://github.com/nipreps/fmriprep/issues/2777#issuecomment-1243532076 on another dataset. I am trying to track this down. Linking to https://github.com/nipreps/fmriprep/issues/2878, as both of these are happening.

@julfou81 Would you mind sharing the voxel sizes and shape of your datasets?

The data set that failed with SyN had the following parameters but strong distortions due to improper shimming values during the acquisition:
dim1		126
dim2		126
dim3		81
dim4		180
datatype	512
pixdim1		1.603175
pixdim2		1.603175
pixdim3		1.600000
pixdim4		2.800000

The data that went through with SyN with correctly estimated deformations had the following parameters:
dim1		100
dim2		100
dim3		70
dim4		359
datatype	512
pixdim1		2.000000
pixdim2		2.000000
pixdim3		2.000000

Met the same issue as above when processing ds004215 using fmriprep 22.0.0. Only some subjects have fieldmap ('IntendedFor' not present). I was expecting --ignore filedmaps --use-syn-sdc to ignore the fieldmaps for those with it and run fieldmap-less correction for all subjects.

 Without `--ignore filedmaps --use-syn-sdc`
	 With `--ignore filedmaps --use-syn-sdc`

 Has fieldmap (sub-ON01016)
	 fieldmap-based SDC ✓
	 no SDC (!)

 No fieldmap (sub-ON01802)
	 no SDC ✓
	 fieldmap-less SDC ✓

All of these did not crash.
Here is the beginning of the output log:
01
<EMAIL_ADDRESS>
This dataset appears to be BIDS compatible.

        Summary:                  Available Tasks:        Available Modalities:
        426 Files, 17.81GB        TOLD                    MRI
        5 - Subjects              VibStim
        3 - Sessions

	If you have any questions, please post on https://neurostars.org/tags/bids.

221122-20:52:30,80 nipype.workflow IMPORTANT:
	 Running fMRIPrep version 22.0.2

         License NOTICE ##################################################
         fMRIPrep 22.0.2
         Copyright 2022 The NiPreps Developers.

         This product includes software developed by
         the NiPreps Community (https://nipreps.org/).

         Portions of this software were developed at the Department of
         Psychology at Stanford University, Stanford, CA, US.

         This software redistributes the versioneer Python package, which is
         Public domain source code.

         This software is also distributed as a Docker container image.
         The bootstraping file for the image ("Dockerfile") is licensed
         under the MIT License.

         This software may be distributed through an add-on package called
         "Docker Wrapper" that is under the BSD 3-clause License.
         #################################################################
221122-20:52:30,115 nipype.workflow WARNING:
	 Previous output generated by version 0+unknown found.
221122-20:52:30,650 nipype.workflow IMPORTANT:
	 Building fMRIPrep's workflow:
           * BIDS dataset path: /work/TOLD.
           * Participant list: ['01'].
           * Run identifier: 20221122-205202_20c3e7f9-ae1e-4e65-bce8-46d14a3bb07f.
           * Output spaces: MNI152NLin2009cAsym:res-native T1w.
           * Pre-run FreeSurfer's SUBJECTS_DIR: /work/TOLD/derivatives/fmriprep22syn/sourcedata/freesurfer.
221122-20:52:32,540 nipype.workflow INFO:
	 B0 field inhomogeneity map will be estimated with  the following 2 estimators: [<EstimatorType.PEPOLAR: 2>, <EstimatorType.PEPOLAR: 2>].
221122-20:52:32,806 nipype.workflow INFO:
	 Using single-band reference file(s) sub-01_ses-post_task-TOLD_run-01_sbref.nii.gz.
221122-20:52:32,806 nipype.workflow INFO:
	 Found usable B0-map (fieldmap) estimator(s) <B0mappost> to correct </work/TOLD/sub-01/ses-post/func/sub-01_ses-post_task-TOLD_run-01_bold.nii.gz> for susceptibility-derived distortions.
221122-20:52:33,339 nipype.workflow INFO:
	 Using single-band reference file(s) sub-01_ses-post_task-TOLD_run-02_sbref.nii.gz.
221122-20:52:33,339 nipype.workflow INFO:
	 Found usable B0-map (fieldmap) estimator(s) <B0mappost> to correct </work/TOLD/sub-01/ses-post/func/sub-01_ses-post_task-TOLD_run-02_bold.nii.gz> for susceptibility-derived distortions.
221122-20:52:33,715 nipype.workflow INFO:
	 Using single-band reference file(s) sub-01_ses-post_task-TOLD_run-03_sbref.nii.gz.
221122-20:52:33,715 nipype.workflow INFO:
	 Found usable B0-map (fieldmap) estimator(s) <B0mappost> to correct </work/TOLD/sub-01/ses-post/func/sub-01_ses-post_task-TOLD_run-03_bold.nii.gz> for susceptibility-derived distortions.
221122-20:52:34,192 nipype.workflow INFO:
	 Using single-band reference file(s) sub-01_ses-post_task-TOLD_run-04_sbref.nii.gz.
221122-20:52:34,192 nipype.workflow INFO:
	 Found usable B0-map (fieldmap) estimator(s) <B0mappost> to correct </work/TOLD/sub-01/ses-post/func/sub-01_ses-post_task-TOLD_run-04_bold.nii.gz> for susceptibility-derived distortions.
221122-20:52:34,556 nipype.workflow INFO:
	 Using single-band reference file(s) sub-01_ses-post_task-TOLD_run-05_sbref.nii.gz.
221122-20:52:34,556 nipype.workflow INFO:
	 Found usable B0-map (fieldmap) estimator(s) <B0mappost> to correct </work/TOLD/sub-01/ses-post/func/sub-01_ses-post_task-TOLD_run-05_bold.nii.gz> for susceptibility-derived distortions.
221122-20:52:34,926 nipype.workflow INFO:
	 Using single-band reference file(s) sub-01_ses-pre_task-TOLD_run-01_sbref.nii.gz.
221122-20:52:34,926 nipype.workflow INFO:
	 Found usable B0-map (fieldmap) estimator(s) <B0mappre> to correct </work/TOLD/sub-01/ses-pre/func/sub-01_ses-pre_task-TOLD_run-01_bold.nii.gz> for susceptibility-derived distortions.
221122-20:52:35,294 nipype.workflow INFO:
	 Using single-band reference file(s) sub-01_ses-pre_task-TOLD_run-02_sbref.nii.gz.
221122-20:52:35,295 nipype.workflow INFO:
	 Found usable B0-map (fieldmap) estimator(s) <B0mappre> to correct </work/TOLD/sub-01/ses-pre/func/sub-01_ses-pre_task-TOLD_run-02_bold.nii.gz> for susceptibility-derived distortions.
221122-20:52:35,663 nipype.workflow INFO:
	 Using single-band reference file(s) sub-01_ses-pre_task-TOLD_run-03_sbref.nii.gz.
221122-20:52:35,664 nipype.workflow INFO:
	 Found usable B0-map (fieldmap) estimator(s) <B0mappre> to correct </work/TOLD/sub-01/ses-pre/func/sub-01_ses-pre_task-TOLD_run-03_bold.nii.gz> for susceptibility-derived distortions.
221122-20:52:36,143 nipype.workflow INFO:
	 Using single-band reference file(s) sub-01_ses-pre_task-TOLD_run-04_sbref.nii.gz.
221122-20:52:36,143 nipype.workflow INFO:
	 Found usable B0-map (fieldmap) estimator(s) <B0mappre> to correct </work/TOLD/sub-01/ses-pre/func/sub-01_ses-pre_task-TOLD_run-04_bold.nii.gz> for susceptibility-derived distortions.
221122-20:52:36,511 nipype.workflow INFO:
	 Using single-band reference file(s) sub-01_ses-pre_task-TOLD_run-05_sbref.nii.gz.
221122-20:52:36,511 nipype.workflow INFO:
	 Found usable B0-map (fieldmap) estimator(s) <B0mappre> to correct </work/TOLD/sub-01/ses-pre/func/sub-01_ses-pre_task-TOLD_run-05_bold.nii.gz> for susceptibility-derived distortions.
221122-20:52:36,931 nipype.workflow INFO:
	 Setting-up fieldmap "B0mappost" (EstimatorType.PEPOLAR) with <sub-01_ses-post_dir-AP_epi.nii.gz, sub-01_ses-post_dir-PA_epi.nii.gz>
221122-20:52:36,947 nipype.workflow INFO:
	 Setting-up fieldmap "B0mappre" (EstimatorType.PEPOLAR) with <sub-01_ses-pre_dir-AP_epi.nii.gz, sub-01_ses-pre_dir-PA_epi.nii.gz>
221122-20:52:42,980 nipype.workflow INFO:
	 fMRIPrep workflow graph with 2506 nodes built successfully.
221122-20:52:58,373 nipype.workflow IMPORTANT:
	 fMRIPrep started!

 Please open a new issue if this is still a problem in 23.2.0a1.


Add -app suffix to JS bundle

I committed with --no-verify because prettier is trying and failing to format the .travis.yml file.

I think this is no longer needed as we now use GitHub actions and the -app suffix was added since then. Thanks!

I think this is no longer needed as we now use GitHub actions and the -app suffix was added since then. Thanks!


WIP: Support gc'ing the CPIO cache

Depends on https://github.com/DeterminateSystems/nix-cpio-generator/pull/3.


Change help icon to info

It looks more appealing to me, but more importantly it conveys intent well

but still keep it named help? hm. i feel like if youre making it info we should rename it to info

Good point. On a personal level, I'm good either way; the issue is mainly because it's certainly not a "question."

"info" does make more sense, i think, compared to "help". but im still not sure what the best solution is. honestly its more like a splash screen or a welcome modal or something idk

Maybe the rocket or play icons with the name "Start"?

"bindings" makes most sense, i think, in light of #158 wanting a different settings modal?


[Accessibility] Microsite - Color Contrast fails to meet WCAG conformance

📜 Description
Example: Selection drop-downs, “are you using Backstage”, privacy info, links in docs text
Users with low vision may find controls inoperable or be unable to read on-page text
Some of these elements returned contrasts of 1.27:1 (such as the privacy paragraph) thus new color pallets may need to be chosen by design
👍 Expected behavior
Color contrast succeeds. One can use Contrast Checker to verify this.
👎 Actual Behavior with Screenshots
(couldn't find an example of the issue of when the audit was first conducted but here is another one)
Example of links:

👟 Reproduction steps

Go to https://backstage.io/
Run the Lighthouse browser extension

Alternatively:

Run the microsite locally
Run the Lighthouse CLI locally when developing new features

👀 Have you spent some time to check if this bug has been raised before?

[X] I checked and didn't find similar issue

🏢 Have you read the Code of Conduct?

[X] I have read the Code of Conduct

Are you willing to submit PR?
None

@emmaindal @vinzscam suggestion changing #36baa2 link color to #15473E work with both #e3e3e3 and #ffffff

adding underline is enough to solve this.


Partial Fill / Eviction Cache Service Interface Modification

Rebase the work of IF modification for partial fill and UCE FSM modification for multi-cycle fill/eviction to me_dev. Tested on unicore with multicycle-fill, and on single core with full cache block fill.

@muwyse , I'll review this thoroughly, but take a look. Especially think about how it will relate to serialization / deserialization in the LCEs

I think the idea is we want to support something like critical word first fill. But, what are the constraints? As the xCE what rules must I follow in terms of ordering fills for a block? What constraints are there in the relationship between dword_width, fill_width, and block_width? How do these relate to the allowed bank_widths and banking schemes in the cache? I think it could be beneficial to limit the allowable configurations to those that make sense. For example, bank_width must be an integer multiple of dword_width, or, fill_width == bank_width.

Excellent questions.  We have the descriptions for this in the Cache Service Interface Doc, but we should put them in the interface specification with this PR

Request changed are maded.
Brief description of multi-cycle fill/eviction are appended in the comment at the top of both d-cache and i-cache.
Additional checks on the condition, fill_width = N*bank_width <= block_width is added in nonsynth_if_verif

Finish rebasing to the latest me_dev. And the accelerator and some other module in flist are fixed


Iminleg rebase

What is iminleg ? For people outside of Intel, this pull request is exceptionally obscure, no description, no commentary (internal reviewed), not even a nice branch name (like upstream_from_legacy_branch)


Persisted Data: Items Added to the Shopping List Disappear on App Closure

Please assign this to me

Done with this please merge the pull request


try with parallel=3

Fixes #
Proposed Changes

increase parallel to 6.

Release Note

is that an issue w/ resources of the machines ?

@matzew unclear. I'm concerned that these are exposing some other issues, hence I'll keep playing with it. It might be a good idea to get more thorough / stress testing in the e2e like eventing with many triggers to see if there are some concurrency issues. Or just start with more stress testing of the channels. Not sure yet, but I think we need more tests :)

/test pull-knative-eventing-contrib-integration-tests

/test pull-knative-eventing-contrib-integration-tests

/test pull-knative-eventing-contrib-integration-tests

/test pull-knative-eventing-contrib-integration-tests

@vaikas I'm having this same issue with the pubsub channel. See https://github.com/google/knative-gcp/pull/590
According to @chizhg this doesn't happen to natss nor imc, so we might be doing something weird in kafka and pubsub...
I have some cycles and will be taking a closer look at pubsub. I'll sync with you in case I see something weird that may also fix kafka...

@nachocano Thanks! Yeah, keep me posted if you find something, I think @matzew was going to take a deep look at the Kakfa channels in general, so hopefully we can get to the bottom of this sooner rather than later :)

I think I found the bug: https://github.com/knative/eventing-contrib/pull/1165

Thanks @pierDipi 👍


Aarch migration

Hi! This is the friendly automated conda-forge-linting service.
I just wanted to let you know that I linted all conda-recipes in your PR (recipe) and found it was in an excellent condition.
I do have some suggestions for making it better though...
For recipe:

License is not an SPDX identifier (or a custom LicenseRef) nor an SPDX license expression.

@hmaarrfk, I think we'll need your help to trigger the rebuild on travis and drone ci.

Hi! This is the friendly automated conda-forge-linting service.
I just wanted to let you know that I linted all conda-recipes in your PR (recipe) and found it was in an excellent condition.

@conda-forge/libpcap this is ready for review again.
If you approve the run_exports piece, we should do a PR against conda-forge-pinning to remove it from there.

Thank you @sodre !


See if ioutilCopy is better

Can fix #8127

@ArthurSens Here are the changes. Let me know how to trigger benchmark runs. Thanks

It has to be a member of the Prometheus org. I'll try to ping someone at the IIRC :slightly_smiling_face:

We do not have benchmark for this as it seems only used in tests.

Sorry @nrchakradhar, looks like I've been leading you on the wrong way.
What are we supposed to do here, then? Is the benchmark provided at the linked issue enough?

If this is only used in unittests, then there's no real need to make it performant - code clarity is generally more important there.

@brian-brazil @roidelapluie I will check once again and if it's use is only in tests, I will close the PR.
It might help to segregate test util code. That can be a different issue and PR.

It is only used in tests, and given that Go test code can't access tests in other modules I don't think we'd gain much here by splitting out this function to a new module.

Agree. Its used only by copyDirs, which is used only in compact_test.go, db_test.go and repair_test.go.
Closing the PR. Will update the same in #8127 as well.
Thanks for all the help and patience.

Sorry @nrchakradhar, looks like I've been leading you on the wrong way.

@ArthurSens Not at all. I learnt a thing to check the utility of the function before optimizing. I also learnt what are the different ways copy can be done.


Proposed fixes for issues found by OpenScanHub

Hi,
we in Fedora and CentOS Stream run static and dynamic analyzer tool called OpenScanHub which scans our components in the OS.
I went through its results for Vim and selected 4 possible issues which might be real issues (I was not able to find a reason why it can't happen, but some circumstances were wild...) - the PR is split into several commits based on which found issues it fixes.
src/spell.c: Protect wres from possible buffer overflow - here is probably some hidden meaning why the execution can't reach the buffer overflow, but it was not clear from the code, so a simple check should do the trick.
src/json.c: Protect nr from integer overflow - IMHO might happen if Vim reads invalid JSON, which includes too large number which would cause the integer overflow.
src/vim9class.c: Fix typo - this looked like a typo - the code tries to find the class where the member is defined, and IMO the second if is for class variables, so the vartype should be VAR_CLASS.
Does it make sense to fix these issues or they are false positives? If it makes sense to fix them, it would be great if the PR was merged into the project.
Thank you in advance!
Zdenek

Thanks. I think the first one makes sense. The second one looks like a false positive. the call to vim_str2nr() is limited to 4 byte len, I don't think nr can therefore become larger than INT_MAX/INT_MIN. The third one looks like a typo indeed.

@chrisbra ready for final review

Thanks. I pushed it


Not working anymore (hotkeys and map layouts)

For some reason the hotkeys do nothing for me, meaning the map layouts don't load, and I can no longer bring up/hide different menus. Sometimes it works, sometimes it doesn't upon restarting the program multiple times, very peculiar.

This generally means it doesn't have admin access. Try running as an admin user and see if that helps. If not, check out the discord for more advice.


posting buffers?

It's says that you support buffers

body: empty        // request body. can be a string, buffer, readable stream

but when i send a simple buffer to httpbin it yields empty
So i tried something else:
    var stream = require('stream');
    var bufferStream = new stream.PassThrough();
    bufferStream.end(buffer);

i used bufferStream as body and send it to httpbin and that was successful

We are likely missing a test case on this, shouldn't be too hard to add when I am available.

fixed in v1.6.0


Feature Request - AllArgsConstructor for native Domain Entities.

#5 Issue Details
Please add param constructors for native domain entities such as Phone and Address
This can be accomplished by including the following annotations on domain entities
@AllArgsConstructor
@NoArgsConstructor

Type of Issue  (check one with "X")
[ ] Bug Report => Please search GitHub for a similar issue or PR before submitting
[X] Feature Request => Please ensure feature is not already in progress
[ ] Support Request => Please do not submit support requests here, instead see: https://discourse.oss.antheminc.com/

Current Behavior
Domain entity creation often results in an unnecessary amount of code individually setting every field.
Expected Behavior
Phone phone = new Phone(Type.CELL, "308-456-7890", "7365")
How to Reproduce the Issue
Steps to Reproduce
Construct a native Nimbus entity.
Code Snippet
See Current Behavior section
Environment Details

Nimbus Version:
1.3.1.M4

Browser:
N/A

I'm not sure if we we were ever planning on removing these objects entirely from the core framework library or not. @soham-anthem @jayantchaudhuri any guidance? If not, I don't think it's a big deal to add this in.

They seem to be used quite frequently, so if they aren't cumbersome to maintain - I'd vote to keep them in.

Perhaps these would be a good candidate for their own project when we start splitting out nimbus-core.
We can consider adding these to a domain-entity-common or similar project.

This change is deployed in 1.3.2.M2

Awesome thanks!


Packs folder doesn't exists

Packs doesn't exists neither in system.json and so in game there are no bestiray, carreers, skills, talents...
Is it normal?

Upon creating a world you should see a pop-up that relates to this. I removed them due to copyright reasons, but I am working with C7 officially to bring them back. In the meantime, if you need assistance in recreating content for your games, contact me on discord.


Parameterize package names

This adds parameters for the following packages:

node_pkg
npm_pkg
dev_pkg

For backwards-compatibility, I have left the dev_package boolean name intact.
Added a little more documentation.
Noted that this has been tested on Red Hat Enterprise Linux 6.
Note: this PR obviates #84

@cmurphy can you cut a new release of this module?


add tests

I've also written a blog post and recorded a video explaining the tests: https://code-maven.com/python-testing-interval-timer


[Feature] Support for v4 associations API

Hubspot have changed their associations API and introduced a v4. This works quite differently to before with extra data needed for creation of the relationship etc.
Can we look to get this supported?

Not looking to implement v4 associations anytime soon. I don't see any benefit for the SDK consumer. As long as Hubspot keeps v3 available and supported, I'm not sure it's worth the effort just yet. That might change down the road.


Update DemoForm

add the checkBox annotation
Requirements

Filling out the template is required. Any pull request that does not include enough information to be reviewed in a timely manner may be closed at the maintainers' discretion.
All new code requires tests to ensure against regressions

Description of the Change

Alternate Designs

Why Should This Be In Core?

Benefits

Possible Drawbacks

Applicable Issues

Coverage remained the same at 0.0% when pulling 201060a96aaa0fc21afc18d6f67df8d3a2de1a3f on Fakhreddine10:patch-13 into 6b9f73a3982e26b166a4a86e84bc5cc65e10036c on JsonSchema-JavaUI:master.


fix warning due to llvm deprecated functions

Ugh I hate that we have tests that fetch things from the network... Rerunning the job


bug: tooltip within stat-title inherits opacity

What version of daisyUI are you using?
v2.50.0
Describe your issue
When you place a tooltip inside of the stat-title class of a stat (for example if you wanted to show a question icon with a tooltip explanation), the tooltip inherits opacity: 0.6 from the stat-title class. This is overridden while the tooltip is inactive, but when it's active it has opacity: 0.6 and looks off. I don't know enough to know if what I'm suggesting is a good alternative, but setting opacity outright within stat-title seems uncharacteristic for DaisyUI. Maybe set the text color with a modified opacity, or add a dedicated css variable?
What browsers are you seeing the problem on?
I've only tested on chrome, but I don't think it's relevant.
Reproduction URL (optional)
https://codepen.io/johnbchron/pen/bGxVPMo

Thank you. Fixed in version 2.50.2


Fwdport/rel7.0 master

@ironman-machine try

Hello @rahulreddy
"try": Success: Try build successfully launched on 'http://ci.ironmann.io/gh/scality/Integration/14510' with the following env. args:
{
    "SCALITY_INTEGRATION_BRANCH": "ultron/master",
    "REPO_NAME": "S3",
    "DEFAULT_BRANCH": "master",
    "SCALITY_S3_BRANCH": "fwdport/rel7.0-master"
}

PR has been updated. Reviewers, please be cautious.

@ironman-machine try

Hello @rahulreddy
"try": Success: Try build successfully launched on 'http://ci.ironmann.io/gh/scality/Integration/14511' with the following env. args:
{
    "SCALITY_INTEGRATION_BRANCH": "ultron/master",
    "REPO_NAME": "S3",
    "DEFAULT_BRANCH": "master",
    "SCALITY_S3_BRANCH": "fwdport/rel7.0-master"
}

:sunny: :+1: circleCI test succeeded!


add a simple Snakemake profile for Slurm

Add a simple Snakemake profile for Slurm.
Adapted from https://github.com/jdblischak/smk-simple-slurm.

@xinyuejohn , i am going to resolve the conflict now

as agreed, this pr will be replace to a new pr, so i close it here

Codecov Report

Merging #142 (5fdc2d3) into master (ae3ce34) will decrease coverage by 4.17%.
The diff coverage is 0.00%.

@@            Coverage Diff             @@
##           master     #142      +/-   ##
==========================================
- Coverage   94.81%   90.65%   -4.17%
==========================================
  Files         113      114       +1
  Lines        3762     3938     +176
==========================================
+ Hits         3567     3570       +3
- Misses        195      368     +173

Flag
Coverage Δ

unittests
90.65% <0.00%> (-4.17%)
:arrow_down:

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files
Coverage Δ

domainlab/utils/hyperparameter_sampling_error.py
0.00% <0.00%> (ø)

domainlab/algos/msels/c_msel.py
100.00% <0.00%> (+15.00%)
:arrow_up:

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

              PyTorch CUDA memory summary, device ID 0                 |

|---------------------------------------------------------------------------|
|            CUDA OOMs: 0            |        cudaMalloc retries: 0         |
|===========================================================================|
|        Metric         | Cur Usage  | Peak Usage | Tot Alloc  | Tot Freed  |
|---------------------------------------------------------------------------|
| Allocated memory      |       0 B  |    3226 MB |   21471 GB |   21471 GB |
|       from large pool |       0 B  |    3221 MB |   21455 GB |   21455 GB |
|       from small pool |       0 B  |      10 MB |      16 GB |      16 GB |
|---------------------------------------------------------------------------|
| Active memory         |       0 B  |    3226 MB |   21471 GB |   21471 GB |
|       from large pool |       0 B  |    3221 MB |   21455 GB |   21455 GB |
|       from small pool |       0 B  |      10 MB |      16 GB |      16 GB |
|---------------------------------------------------------------------------|
| GPU reserved memory   |     980 MB |    3480 MB |    3480 MB |    2500 MB |
|       from large pool |     976 MB |    3468 MB |    3468 MB |    2492 MB |
|       from small pool |       4 MB |      12 MB |      12 MB |       8 MB |
|---------------------------------------------------------------------------|
| Non-releasable memory |       0 B  |  423934 KB |    6454 GB |    6454 GB |
|       from large pool |       0 B  |  421328 KB |    6432 GB |    6432 GB |
|       from small pool |       0 B  |    5193 KB |      21 GB |      21 GB |
|---------------------------------------------------------------------------|
| Allocations           |       0    |    1036    |    1591 K  |    1591 K  |
|       from large pool |       0    |     111    |     721 K  |     721 K  |
|       from small pool |       0    |    1004    |     869 K  |     869 K  |
|---------------------------------------------------------------------------|
| Active allocs         |       0    |    1036    |    1591 K  |    1591 K  |
|       from large pool |       0    |     111    |     721 K  |     721 K  |
|       from small pool |       0    |    1004    |     869 K  |     869 K  |
|---------------------------------------------------------------------------|
| GPU reserved segments |      13    |      69    |      69    |      56    |
|       from large pool |      11    |      63    |      63    |      52    |
|       from small pool |       2    |       6    |       6    |       4    |
|---------------------------------------------------------------------------|
| Non-releasable allocs |       0    |     115    |     815 K  |     815 K  |

/var/spool/slurmd/job12367024/slurm_script: line 3: 224719 Killed                  DOMAINLAB_CUDA_START_SEED='' /home/aih/xudong.sun/anaconda3/bin/python -m snakemake --snakefile '/home/aih/xudong.sun/DomainLab/domainlab/exp_protocol/benchmark.smk' --target-jobs 'run_experiment:index=5' --allowed-rules 'run_experiment' --cores 'all' --attempt 4 --force-use-threads --resources 'mem_mb=1000' 'mem_mib=954' 'disk_mb=1000' 'disk_mib=954' --wait-for-files '/home/aih/xudong.sun/DomainLab/.snakemake/tmp.g36699hz' 'zoutput/benchmarks/test_bloodcell_benchmark/hyperparameters.csv' --force --keep-target-files --keep-remote --max-inventory-time 0 --nocolor --notemp --no-hooks --nolock --ignore-incomplete --keep-incomplete --rerun-triggers 'params' 'mtime' 'input' 'code' 'software-env' --skip-script-cleanup --use-conda --conda-frontend 'mamba' --conda-base-path '/home/aih/xudong.sun/anaconda3' --wrapper-prefix 'https://github.com/snakemake/snakemake-wrappers/raw/' --configfiles '/home/aih/xudong.sun/DomainLab/examples/yaml/test_helm_benchmark.yaml' --printshellcmds --latency-wait 60 --scheduler 'greedy' --scheduler-solver-path '/home/aih/xudong.sun/anaconda3/bin' --default-resources 'mem_mb=1000' 'disk_mb=max(2*input.size_mb, 1000)' 'tmpdir=system_tmpdir' 'partition=gpu_p' 'qos=gpu' --mode 2
slurmstepd: error: Detected 1 oom-kill event(s) in StepId=12367024.batch cgroup. Some of your processes may have been killed by the cgroup out-of-memory handler.

@xinyuejohn @rahulbshrestha , which configuration did you use you have't met this error?
what is the diffrent between mem_mb=1000 and --mem=60G?
I changed mem_mb=10000 and will see the results

@smilesun this PR is a bit confusing, are the files such as "hyperparameter_sampling_error.py" supposed to be included or not? Also, did running this branch work for you as compare to the branch in https://github.com/marrlab/DomainLab/pull/146?

@smilesun this PR is a bit confusing, are the files such as "hyperparameter_sampling_error.py" supposed to be included or not? Also, did running this branch work for you as compare to the branch in #146?
@rahulbshrestha
Hi,

branch #146 has missing files as I wrote in the PR.  But you can resolve the issues I posted in that PR to update the files in this PR.
hyper**_error should be removed, I am just curious to see what is inside there
it works on my laptop by the following

rm zoutput # first clean up the output directory
git checkout snakemake_slurm
./examples/benchmark/run_snakemake.sh

the last error is what I created deliberately,  for issue
https://github.com/marrlab/DomainLab/issues/152
where on my laptop the rule "agg_partial_results" does not work.
Did you met any problem on your laptop? Could you post the error message in an issue?

@rahulbshrestha , sorry I think I misunderstood, I was trying to resolve the other issue which can be reproduced on laptop, i guess we are now talking the helm cluster? Did you meet any problem there? What is the difference of the current  version to the version you have used for generating the results for the presentation?

@rahulbshrestha , sorry I think I misunderstood, I was trying to resolve the other issue which can be reproduced on laptop, i guess we are now talking the helm cluster? Did you meet any problem there? What is the difference of the current version to the version you have used for generating the results for the presentation?

Hey @smilesun , nope there was no issue in the Helmholtz cluster, we ran the latest changes from this branch.

Error in rule run_experiment:
output: zoutput/benchmarks/test_bloodcell_benchmark/rule_results/5.csv
[Wed Mar  8 12:58:29 2023]
Error in rule run_experiment:
jobid: 6
input: zoutput/benchmarks/test_bloodcell_benchmark/hyperparameters.csv
output: zoutput/benchmarks/test_bloodcell_benchmark/rule_results/2.csv
[Wed Mar  8 12:58:29 2023]
rule run_experiment:
input: zoutput/benchmarks/test_bloodcell_benchmark/hyperparameters.csv
output: zoutput/benchmarks/test_bloodcell_benchmark/rule_results/2.csv
jobid: 6
reason: Missing output files: zoutput/benchmarks/test_bloodcell_benchmark/rule_results/2.csv; Input files updated by another job: zoutput/benchmarks/test_bloodcell_benchmark/hyperparameters.csv
wildcards: index=2
resources: mem_mb=60000, mem_mib=57221, disk_mb=1000, disk_mib=954,

snakemake: error: Couldn't parse config file: while parsing a block mapping
  in "examples/yaml/slurm/config.yaml", line 2, column 1
expected <block end>, but found '<scalar>'
  in "examples/yaml/slurm/config.yaml", line 6, column 5

@rahulbshrestha ,
for the current commit, if i run
./examples/benchmark/run_snakemake_slurm_conf_seed.sh examples/yaml/test_helm_benchmark.yaml 0
I got

Error executing rule run_experiment on cluster (jobid: 7, external: Submitted batch job 11333094, jobscript: /home/aih/xudong.sun/DomainLab/.snakemake/tmp.8lqmko_1/snakejob.run_experiment.7.sh). For error details see the cluster log and the log files of the involved rule(s).
Trying to restart job 7.
Select jobs to execute...

there is no other useful information in the logs folder, does the same command work for you?
which command did you use?

after fixing the path int the last commit, i got error which i can not diagnose from the --error output file, which read
cat run_experiment-index\=5-11335086.err
Building DAG of jobs...
Using shell: /usr/bin/bash
Provided cores: 2
Rules claiming more threads will be scaled down.
Provided resources: mem_mb=1000, mem_mib=954, disk_mb=1000, disk_mib=954
Select jobs to execute...

[Wed Mar  8 19:12:32 2023]
rule run_experiment:
    input: zoutput/benchmarks/test_bloodcell_benchmark/hyperparameters.csv
    output: zoutput/benchmarks/test_bloodcell_benchmark/rule_results/5.csv
    jobid: 0
    reason: Missing output files: zoutput/benchmarks/test_bloodcell_benchmark/rule_results/5.csv
    wildcards: index=5
    resources: mem_mb=1000, mem_mib=954, disk_mb=1000, disk_mib=954, tmpdir=/tmp, partition=gpu_p, qos=gpu

/home/aih/xudong.sun/anaconda3/lib/python3.9/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
  warnings.warn(
/home/aih/xudong.sun/anaconda3/lib/python3.9/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.
  warnings.warn(msg)
/home/aih/xudong.sun/anaconda3/lib/python3.9/site-packages/torch/nn/_reduction.py:42: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
  warnings.warn(warning.format(ret))
/home/aih/xudong.sun/anaconda3/lib/python3.9/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: Metric `AUROC` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.
  warnings.warn(*args, **kwargs)
/home/aih/xudong.sun/anaconda3/lib/python3.9/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: No positive samples in targets, true positive value should be meaningless. Returning zero tensor in true positive score
  warnings.warn(*args, **kwargs)

I am interested to know what else is need to make those run.

/var/spool/slurmd/job11335014/slurm_script: line 3: 1371902 Killed                  DOMAINLAB_CUDA_START_SEED='0' /home/aih/xudong.sun/anaconda3/bin/python -m snakemake --snakefile '/home/aih/xudong.sun/DomainLab/domainlab/exp_protocol/benchmark.smk' --target-jobs 'run_experiment:index=3' --allowed-rules 'run_experiment' --cores 'all' --attempt 2 --force-use-threads --resources 'mem_mb=1000' 'mem_mib=954' 'disk_mb=1000' 'disk_mib=954' --wait-for-files '/home/aih/xudong.sun/DomainLab/.snakemake/tmp.di7gu163' 'zoutput/benchmarks/test_bloodcell_benchmark/hyperparameters.csv' --force --keep-target-files --keep-remote --max-inventory-time 0 --nocolor --notemp --no-hooks --nolock --ignore-incomplete --keep-incomplete --rerun-triggers 'params' 'mtime' 'software-env' 'code' 'input' --skip-script-cleanup --use-conda --conda-frontend 'mamba' --conda-base-path '/home/aih/xudong.sun/anaconda3' --wrapper-prefix 'https://github.com/snakemake/snakemake-wrappers/raw/' --configfiles '/home/aih/xudong.sun/DomainLab/examples/yaml/test_helm_benchmark.yaml' --printshellcmds --latency-wait 60 --scheduler 'greedy' --scheduler-solver-path '/home/aih/xudong.sun/anaconda3/bin' --default-resources 'mem_mb=1000' 'disk_mb=max(2*input.size_mb, 1000)' 'tmpdir=system_tmpdir' 'partition=gpu_p' 'qos=gpu' --mode 2
slurmstepd: error: Detected 1 oom-kill event(s) in StepId=11335014.batch. Some of your processes may have been killed by the cgroup out-of-memory handler.

@smilesun @rahulbshrestha I am currently testing this on Helmholtz cluster. Will pin you again once I make it run

the proces was killed by out-of-memory signal


Support geojson output

http://geojson.org/ for details
https://geocoder.opencagedata.com/api#request

Close because I'm not going to need this.


⚠ Processors has degraded performance

In 5cc460b, Processors ($STATUS_URL) experienced degraded performance:

HTTP code: 200
Response time: 59 ms

Resolved: Processors performance has improved in a0d9483.


adding risk component dropdowns for mobile [#166]

this is my first attempt at switching to risk component dropdowns on mobile. I couldn't figure out a way to do it without some amount of duplication, so now, there is just both the columns and the dropdowns and we selectively hide one or the other based on whether we are on medium and up or not.
let me know if I should go back to the drawing board or whether this is something we could look into making more singular.
close #166
┆Issue is synchronized with this Trello card by Unito

I've extracted the remaining issues into issue #183 so that we can merge this in and I can have it deployed to staging for Design Developer Live Sync 2.


fix: Fix error caused when input data is mixed with datetime and string types

@AkiraUra @kimusaku
Fix error caused when input data contains a mix of datetime and string types that rarely convert to datetime types.
Please check.

@tashiro-akira Could you make this PR pass the lint check? Thanks.

You can check the error by creating and running the following main.py
import numpy as np
import pandas as pd
from sapientml import SapientML

from pandas.api.types import infer_dtype
from pandas.core.dtypes.common import is_bool_dtype
import datetime

df = pd.DataFrame({'a': [1,2]*10, 'b': ["moji"]*19+[datetime.datetime.now()], 'y': [1,0]*10})

cls_ = SapientML(
    target_columns=['y'],
    task_type='classification',
)
cls_.fit(
    training_data=df,
)

All the points of the review have been reflected.


Create SG modules for Kubernetes clusters

This PR picks up with @lpaulmp left off in his PR for #78 (https://github.com/fpco/terraform-aws-foundation/pull/82), and primarily renames the variables and updates the use in the public/private kube-stack examples env, but also has a few more subtle changes, like ensuring that the CIDR range used by the load balancer SG is configurable through a variable (<IP_ADDRESS>/0 is a fine default, but with a variable, a user of the module can override it). Same with the api_port for kubernetes load balancer and controllers.

@ketzacoatl Looks good to me :shipit:, awesome work


Check if we can call youtube api

https://developers.google.com/youtube/v3/docs#Playlists

this is totally deprecated..not possible


[TEVA-2141] fix padding on all page layouts

https://dfedigital.atlassian.net/browse/TEVA-2141
this is is still WIP as i want to test this in preview env
this fixes 2 things

the padding on all pages this is now not done with a controller overide but just using default containers and the only padding is applied to all page titles. basically a load of weird margin overides for controllers have been removed and this feels like right thing to do as it was too brittle
The notification component causes complications with background colours and so this has been refactored including use of a renamed full-mast-width mixin that is used only for this full width with a background
markup slightly altered in notifications so the dismiss link is positioned correctly (its actually teva-2145 too)

Review app deployed to https://teaching-vacancies-review-pr-3020.london.cloudapps.digital

Notification backgrounds (colour and size) still seems a bit dodgy

i found the job alert tab background for notification was incorrect else i can see a problem @cpjmcquillan


chore(zipkin): export ExporterConfig

Fixes https://github.com/open-telemetry/opentelemetry-js/issues/1453

Please fix the description

@shivkanya9146 I updated the issue description to mention the issue it fixes. in the future can you make sure to make descriptive PR descriptions so that people know what they are reviewing?

@shivkanya9146 I updated the issue description to mention the issue it fixes. in the future can you make sure to make descriptive PR descriptions so that people know what they are reviewing?

ok, will make will sure in future.

Title says jaeger but fiz is actually applied to zipkin (opposite of #1478)

Title says jaeger but fix is actually applied to zipkin (opposite of #1478)

Done

@shivkanya9146 can you please resolve the conflicts ?


Swipe to go back fails when Scroll-Reverser is enabled

macOS High Sierra 10.13.5, MacBook Pro 15in
When Reverse Scrolling is enabled, you can no longer swipe to the left to go back on the trackpad. Instead the window momentarily shifts but then snaps back to the original position and freezes. Only happens in Safari, Chrome works fine.

10.13.6 - no problem with Safari.

MacOS 10.13.6, Safari 11.1.2, Scroll-Reverser 1.7.6: Same problem as @CaptainZ1

Do those having this problem you have "Reverse Horizontal" enabled?

Ah scratch that. Is this two finger swipe on a trackpad?

The two finger one. What's the difference, by the way?

I've managed to fix the issue now. I believe it was a problem with the settings on the app rather than the app, but not sure. My settings are now: Scroll Direction Natural, and Reverse Vertical checked, but not Reverse Horizontal.

I've managed to fix the issue now. I believe it was a problem with the settings on the app rather than the app, but not sure. My settings are now: Scroll Direction Natural, and Reverse Vertical checked, but not Reverse Horizontal.

Same problem with MacBook pro 16 2019 but solution worked.
Just to clarify as initially I wasn't understanding, make sure natural scroll is selected in normal system preferences on Mac, and use scroll reverser to only reverse the mouse (and/or tablet)

I'm seeing this issue in chrome and safari.
Settings - Trackpad

Scroll reverser

With this enabled, when I try to 2 finger swipe from left to right, it does not go back.
When I swipe slightly to the right, then left it swipes back.
Ideally, 2 finger swipe from left to right works as expected.

For better results with gestures, set Natural scrolling in system prefs, and set Reverse Mouse instead of Reverse Trackpad in Scroll Reverser

Thanks for the quick response!
I have a specific use case that enabling both natural scrolling and reverse trackpad solves, bar this one issue.
Is there anywhere in the the code I could look at that could be causing the gesture issues or is it more of an OS level issue?

Reverse Horizontal instead might also do the trick.
Essentially the OS's gesture detection and reversing the scrolling events don't go well together.
Basically if you have it set up to reverse horizontal trackpad scrolling it will probably mess up the gestures.

Understood, thanks Nick.


normalize reset으로 변경

🤔 해결하려는 문제가 무엇인가요?

close #134

🎉 변경 사항

emotion normalize를 지웠어요

reset css를 가져와 사용했어요

스토리북에 적용된 것들은 1차적으로 확인했어요

Codecov Report
Patch and project coverage have no change.

Comparison is base (340b56e) 95.71% compared to head (a695aae) 95.71%.

Additional details and impacted files
@@           Coverage Diff           @@
##             main     #136   +/-   ##
=======================================
  Coverage   95.71%   95.71%
=======================================
  Files          32       32
  Lines         210      210
  Branches       41       41
=======================================
  Hits          201      201
  Misses          9        9

:umbrella: View full report in Codecov by Sentry.
:loudspeaker: Do you have feedback about the report comment? Let us know in this issue.


web spider

Please see the patch commit at https://github.com/OpenBMB/ChatDev/commit/8619119d8420329b30332833170a80d2f54273ef
Make sure your PR and merge are done after testing.
@Alphamasterliu @yu-shaonian

"ghcr.io/wxw-matt/devcontainer-features/script_runner:0": {}


Is version 2.4.6 compatible with firmware 3.5.0?

Could someone advise if master version 2.4.6 is compatible with FW version 3.5.0 on the Sonoff devices?
I am still running version 8aaa186 which corrected issues with the new FW 3.5.0.
Thanks

No


Remove the need for a prompt when setting up unit tests

This change allows the script to run without any input from the user and, in turn, lets me set up a docker container ready to run tests. Please merge (or I will if @ticoann is on vacation)

Seangchan should be flying at this time, so I just went ahead and merged


Incompatability with the mod Linear XP Levels.

Link to the incompatible mod: https://www.curseforge.com/minecraft/mc-mods/linear-xp-levels
What happens:
Basically whenever you store XP and right-click to get it back from the tome all the experience gets deleted.
video showing what happens:
https://www.youtube.com/watch?v=C3s-dN_xUFg&ab_channel=Romoslayer2.0
Ways to add compatibility:
Could add to the config and allow users to choose between storing experience points or levels.
XP Tome mod version: 1.16.5-2.1.4
Forge version: 1.16.5-36.2.20

That mod does not properly update all the player's XP values. This is not on me to fix.


Make matching case insensitive

If I understand correctly, the bot matches titles and bodies in a case sensitive manner. It means that [WIP] doesn't match [wip]. Could you please make the matching process case insensitive (by applying toLoserCase() to titles, bodies and tokens)?

This sounds good as an optional configuration, something like caseSensitive: false, with it defaulting to true

It works awesome, thank you!


[CT-1057] [Feature] Dynamic Source.yml

Is this your first time submitting a feature request?

[X] I have read the expectations for open source contributors
[X] I have searched the existing issues, and I could not find an existing issue for this feature
[X] I am requesting a straightforward extension of existing dbt functionality, rather than a Big Idea better suited to a discussion

Describe the feature
Context
Since the rollout of the Union Feature in a number of Fivetran dbt packages, I've seen users have an increasing apatite for adopting the feature in the compatible packages as well as request the feature in others. This feature is great for users who have multiple datasets that are similar (maybe the user is an agency, consultancy, or company with many separated business units). However, there is one major flaw with this feature in the Fivetran dbt packages, it breaks dbt convention by not generating a source.yml for each schema/database the user passes through. Therefore, the user is not able to effectively test freshness or generate an accurate DAG 😞.
The Fivetran dbt packages with this feature use only one source.yml as a template to then be leveraged in a custom macro that ultimately passes a list of relations towards dbt_utils.union_relations(). This works great during dbt run as I mentioned, however the DAG then shows no relation to the sources (see the image below).

However, when manually adding multiple sources and naming them properly, you can see the sources are properly defined! 🎉

This is great, but I only had to define two sources. What happens when tomorrow we add another Xero dataset (in this case)? How about in a month when our company grows and we add 100 other Xero datasets?! Managing all those sources and creating the same source.yml time after time can become quite unwieldy and is not ideal. It would be great if we can somehow dynamically generate sources from a single yml file 🤯!
Feature Request
It would be great if there was a way to use Jinja (or some other tool) to dynamically generate the same source.yml for multiple datasets that are the same. I am envisioning something like the following:
## xero_schema = ['xero_mx','xero_ca']

sources:
  - name: "{% for name in var('xero_schema') %}name{% endfor %}"
    schema: "{% for schema in var('xero_schema') %}schema{% endfor %}"
    database: "{{ var('xero_database', target.database) }}"
    tables:
      - name: account

The above does not work, but I imagine some way to leverage dynamic jinja to allow for a single source.yml to be reused multiple times if the structure of the schema is the same.
Describe alternatives you've considered
Manually writing the source.yml for each source. This does work, but is not scalable.
Who will this benefit?
dbt users whose companies relate to the consultancy, agency, or high growth models and see the same datasets being added over time.
Are you interested in contributing this feature?
I don't feel equipped to solve this issue, but open to helping however I can.
Anything else?
Here is the fivetran_utils macro that is called within the custom macro identified in the "Describe the Feature" section. You can see the bulk of the efforts are in place to pass a list of relations to dbt_utils.union_relations().

@fivetran-joemarkiewicz Thanks for opening — this sounds a lot like https://github.com/dbt-labs/dbt-core/issues/3862! Though that issue has been closed as stale, it's never really left my mind.
I don't have any perfect remedies to share, but I can say that this adds onto an interesting conversation of late: How should users—or people, such as package authors, building more complex products on top of dbt—programmatically generate dbt project resources?
Patterns that exist today
yaml anchors
Worth stating for anyone totally new: In the simplest case, where you're defining your own sources that happen to have most of their properties in common, we do support slightly DRYer source creation, by means of yaml anchors.
sources:
  - &xero_base
    name: xero_mx
    database: "{{ var('xero_database', target.database) }}"
    tables:
      - name: account
      - name: contact
      - name: ...
  - <<: *xero_base
    name: xero_ca
  - <<: *xero_base
    name: xero_another_one

That doesn't give you, as package author, the programmatic means to pull just a variable list from the user (['xero_mx','xero_ca']) and populate sources accordingly. It also requires manual action each time a new Xero source (schema) comes into being.
externalize it?
If the column spec for every xero_*.account table is exactly the same, why not just store them all together, in one big xero.account table? Is it actually useful to view these as distinct sources within dbt? Yes, that would mix xero_mx data with xero_ca data, but it would be simple enough to add a new metadata column identifying which source table each one comes from.
This sounds a lot like the Union Feature you've built, though I'm thinking it could be done one step further up the stack: while staging/loading an external table. Most data warehouses can create external tables that map to specific files in S3/GCS/Blob/etc, with specific file name patterns to identify candidates.
Imagine, for Snowflake (using the syntax from the dbt-external-tables package):
sources:
  - name: xero
    database: "{{ var('xero_database', target.database) }}"
    tables:
      - name: account
         external<EMAIL_ADDRESS>         pattern: ".*account"
         partitions:
           - name: xero_connector
             data_type: string
             expression: substr(metadata$filename, ..., ...)  # no need to specify — Snowflake will infer
         columns:
           # common for all Xero connectors
           - name: ...
       - name: contact
          external: ...

This would yield one dbt source, mapped to one external table, for each Xero object (account, contact, invoice, etc), including the data from all Xero connectors.
Future
"union feature" for sources?
A future way to do this, without requiring an external table in the mix, might be to enable dbt to create its own simple "union" feature for sources:
## xero_schema = ['xero_mx','xero_ca']

sources:
  - name: xero
    database: "{{ var('xero_database', target.database) }}"
    union_schemas: ['xero_mx', 'xero_ca']
    tables:
      - name: account
      - name: contact

Then, dbt would "create" each source as:
create table raw.xero.account as (
    select * from raw.xero_mx.account
    union all
    select * from raw.xero_ca.account
)

We could use the slightly trickier logic in dbt_utils.union_relations, to handle any discrepancies in column specs—although part of the idea here is that they should really be identical.
I'd want to be quite limited about this functionality, to maintain the clear distinction between sources as simple "pointers" to raw data, versus models as vehicles for data transformation. External tables as sources already pushes the envelope a teensy bit.
programmatic resource creation
If you really do want one project resource per unique table, and want to avoid 1000s of repetitive lines of code, then there really is no solution other than programmatically generating them.
There's a lot of prior discussion about this in: https://github.com/dbt-labs/dbt-core/discussions/5101

The above does not work, but I imagine some way to leverage dynamic jinja to allow for a single source.yml to be reused multiple times if the structure of the schema is the same.

I have a belief, ever-more strongly held, that while we allow Jinja expressions to programmatically configure project resources, we should not extend Jinja to programmatically create project resources. This is a subtle distinction. Where this is allowed:
models:
  - name: my_model
    tests:
      - unique
      - not_null:
          severity: "{{ var('severity') }}"

This should not be, because the tests field actually instantiates net-new project resources:
models:
  - name: my_model
    tests: "{{ var('tests') }}"

The same would go for:
{% for var in [some, list] %}
   {% model model_{{ var }} %}
      ...
   {% endmodel %}
{% endfor %}

At this point, I just don't see us ever supporting that, to be honest.
Of course, there are people who have found the need to programmatically template / generate dbt project code. They've done so using their own scripting tools, and with dbt project files (yaml + SQL) as their compile target.
Something we have been thinking about more recently: What if this is a case for real code generation? Another interface into dbt project resources, written in Python, such that you (as an end user) could tap into the same "internal manifest" APIs that dbt uses when it parses the code in your project?
Imagine something like:
xero_schema = ['xero_mx','xero_ca']
xero_tables = ['account', 'contact', ...]

# absolutely imaginary code
for schema in xero_schema:
    for table in xero_tables:
        new_source = (
            source_name = schema,
            name = table,
            database = var('xero_database', target.database)
            ...
        )
        dbt.Manifest.sources.add(new_source)

This will be tricky to think through—parsing order becomes all the more confusing; it significantly expands the boundaries of what "dbt-only" code can do—but there are enough legitimate use cases among "superusers" that it's not something I'm willing to dismiss out of hand.

In any case, I'm going to convert this one to a discussion. I don't foresee us taking any immediate steps in these directions, except by continuing to invest in the programmatic interfaces that would make more feasible some version of the last wild-eyed idea.


possible to change minimum signatures?

Hi,
I was wondering if it is possible to change the minimum number of signatures?
I am running sigprofiler extractor on ~400 tumour samples with minimum signatures = 1 and maximum signatures = 25. Unfortunately, my job timed out at 10 signatures. Is it possible to set the minimum number of signatures to 11 in order to continue extracting signatures 11-25? I tried setting the minimum_signatures parameter to 11, but when I did this it stayed at 1 in the JOB_METADATA.txt file. So I tried also manually changing minimum_signatures to 11 in the JOB_METADATA.txt but again, when I ran the job it changed back to 1 in the metadata file.
Any help with this would be greatly appreciated!
Best Wishes
Kerstin

Hi @kerstinth,
Please use sigProfilerExtractor(...,minimum_signatures=11, maximum_signatures=25) in order to set the maximum and minimum number of signatures for the extraction. You will want to make sure that the script you are running is updated to reflect these changes.

Please reach out if you encounter any more issues.


Check sep24 interactive URL

Related to #128.
We realize this will not fix the issue the user may see from waiting too much to click the second confirmation button.
This changes will refresh the sep24 url every 20 seconds until the user actually clicks on the button.

@TorstenStueber it merged well. I imagine that the behavior of the label "Confirming" now that we refresh the sep24 first process is that upon first successful response, we keep display of "Enter details".

I tested, works correctly.


8.41.3

Checklist

[x] Used a personal fork of the feedstock to propose changes
[x] Reset the build number to 0 (if the version changed)
[x] Re-rendered with the latest conda-smithy (Use the phrase @conda-forge-admin, please rerender in a comment in this PR for automated rerendering)
[x] Ensured the license file is being packaged.

This pull request updates framel package to version 8.41.3, which includes a bug fix to the FrCheck tool introduced in version 8.40 (https://git.ligo.org/virgo/virgoapp/Fr/-/commit/33770c36c81b428ee87d70558802726f00d0f11e) and has been requested for deployment onto the IGWN Clusters.

Hi! This is the friendly automated conda-forge-linting service.
I just wanted to let you know that I linted all conda-recipes in your PR (recipe) and found it was in an excellent condition.


Support for CsvMapper?

@pjfanning does this support CsvMapper?  Seems like not, but maybe you can suggest how to apply ScalaReflectExtensions to a CsvMapper instance or builder?

I think CsvMapper needs more than this but try
object CsvMapperScalaReflectExtensions {
  def ::(mapper: CsvMapper) = new Mixin(mapper)

  final class Mixin private[CsvMapperScalaReflectExtensions](mapper: CsvMapper)
    extends CsvMapper(mapper) with ScalaReflectExtensions
}

That will allow you to do this:
  private def newCsvMapperWithScalaReflectExtensions: CsvMapper with ScalaReflectExtensions = {
    CsvMapper.builder().addModule(DefaultScalaModule).build() :: CsvMapperScalaReflectExtensions
  }

I suspect more is needed.

I've added a test using #3 - note the use of javaType in that test - the deserialization will probably not work if you create CsvSchemas using Class or TypeReference.

I looked again and the new test actually fails. I suggest you have a look at https://github.com/FasterXML/jackson-module-scala/wiki/FAQ#deserializing-optionint-seqint-and-other-primitive-challenges and some of the workarounds described there. I don't think this module is useful for you at the moment.

Thanks for taking a look @pjfanning. I did wind up using the workarounds suggested in that faq and it got the job done.

v2.13.4 has changes that might help. In my testing, with CsvMapper, it seems to work well with just jackson-module-scala. If anyone can provide some examples where CsvMapper needs more than jackson-module-scala and where jackson-scala-reflect-extensions doesn't help, I will look at them.

@pjfanning, thanks for that note. Tried v2.13.4. It seems to work for serialization, but not deserialization of csv. I've got a generated avro specific record class (generated from avro hugger). The avro record has an Option[Int] and Option[Double] in it. Serializing json and csv works. Deserializing json works. Deserializing csv doesn't. Adding in scala reflect extensions doesn't work either. But adding @JsonDeserialize(contentAs...) manually works.


Separate files in cmd/

With the additions in #72  https://github.com/kinvolk/kube-spawn/blob/dongsu/enlarge-storage-pool/cmd/kube-spawn/kube-spawn.go is already ~500 lines long.
In the future we want to add even more commands:
#38 #54
We should split it into multiple files.

Covered by #80