Add Fixes for Post-Interrupt Hook Feature

This is a rework of https://github.com/almond-sh/almond/pull/1186 to make the post-interrupt hook feature work as intended:

Added abstract functions to ‘JupyterApi.scala’ to make interface visible to Ammonite kernel object
Added API usage examples to ‘api-jupyter.md’
Added convenience functions for facilitating JupyterApi instance access,
since it is hard to pass around through function/method calls. In case at
hand, we need to access it from methods ‘interruptible’ and ‘interrupt’ in
class ‘Execute’
Added a label field to each registered hook, since Scala lambda functions
are not easy to print in human readable form
Did local testing of API  both when sending interrupt messages via SIGINT or via
zeromq message, i.e. without/with '--interrupt-via-message’)

Note: Contrary to execution hooks, interrupt hooks do not at present funnel output
from one hook into the input of the next in the chain.
Please find screenshots from local testing attached below.

@piiteraq Going to merge and cut a release. Just tell me if you have an opinion about the extra commits I pushed.

Hi @alexarchambault - Thank you so much for the review and the fixes - I agree on all of them. I had difficulties figuring out how to pass the JupyterApi instance down through the call chain through normal parameter passing, so added the instance setter/getter and thought you might have a better idea, which indeed turned out to be the case. Also, you fixed the problem I had with accessing the logger in the proper location.  Thus, please proceed with the release. Thx --


[FLINK-8958][tests] Port TaskCancelAsyncProducerConsumerITCase to flip6

What is the purpose of the change
This PR ports the TaskCancelAsyncProducerConsumerITCase to flip6. The existing test was renamed to TaskCancelAsyncProducerConsumerITCase, and a ported copy was added.

merging.


Entity Embedding Initialization

Hello, I found the following error while performing entity embedding initialization, how can I change the code?

Hello, you can try to run the python file with single GPU

I only have one gpu

Is your CUDA available in the current python interpreter setting?
you can check by the code, torch.cuda.is_available()

Hi, I think this problem might be caused by the inaccurate use of the cuda device.
You can first check the pytorch_lightning  package version and then make sure the correct use of cuda id.


Failed to build physical query: some stages are broken

Failed: : Error: Execution, code: 1060
:26:1: Error: Failed to build physical query: some stages are broken
https://proxy.sandbox.yandex-team.ru/6702038226/index.html#suites/9f2f1bee336a2125852e724942907211/c548d8ce66baf99/
Tpcds100.Query39

https://github.com/ydb-platform/ydb/pull/7308


(#117) Remove private 'v2' refs from Go-module root-dir path

This commit mildly reworks Go-package module dir-path names to remove name sub-strings that were added to track dev-history.  A generic name
"certifier-framework-for-confidential-computing/certifier_service/certprotos"

is being used for go.mod .
All CI-tests seem to run clean with this name change, so it's expected that the Certifier Service build-processes should work correctly on all other platforms, such as Gramine, OE or SEV-enabled hardware.

In addition to CI-tests, following manual tests succeeded on platform-specific hardware sample programs:

On SGX machine:

LOCAL_LIB=/usr/local/lib64 ./run_example.sh simple_app_under_oe setup_with_auto_policy_generation_for_OE
LOCAL_LIB=/usr/local/lib64 ./run_example.sh simple_app_under_oe run_test

succeeded as:
run_example.sh: simple_app_under_oe: Running run_simple_app_under_oe_as_client_make_trusted_request
+ set +x

+ ./host/host enclave/enclave.signed run-app-as-client /home/sgx/agurajada/certifier-framework-for-confidential-computing/sample_apps/simple_app_under_oe/app1_data
Using data_dir: /home/sgx/agurajada/certifier-framework-for-confidential-computing/sample_apps/simple_app_under_oe/app1_data/
read_file_into_string: Can't size input file
oe_Init: No pem cert chain file. Assume empty endorsement.
running as client
Client peer id is Measured-533510290246a9c3777ed006262d1a8469b11116b2945ad089ccbb982728c50d
Client peer cert is:
Server peer id is Measured-533510290246a9c3777ed006262d1a8469b11116b2945ad089ccbb982728c50d
Server peer cert is:
SSL server read: Hi from your secret client

SSL client read: Hi from your secret server

run-app-as-client succeeded!

HI, @rgerganov -- This is the review for the go.mod namespace cleanup we chatted about in slack.
Ye is really busy .. so I'd appreciate it if you could take a look at this change.
Unfortunately, I can't seem to assign this to you directly as I don't see your GitHub handle in the list of reviewers.

You may want to change the module name from certifier-framework-for-confidential-computing/certifier_service to github.com/vmware-research/certifier-framework-for-confidential-computing/certifier_service. I understand this is very long name due to the unfortunate naming of this repo but it will allow people to fetch the module with go get. You can find more information how go get works with version control systems here. I think the same applies to go install as well.


[5.3] Create option for disable logging

Add an option to completely disable logging, setting the option "log" to "null" at config.
If accepted, docs and stub should be updated.
Thanks.

Understood, removed from contract, though I'm not certain whether it should be this way or added to the contract and appointed to 5.4. The way it's at this moment would let the option only available to Monolog, which should be fine anyway.
Would you suggest a "global" null handler for all the loggers?
Thanks.

There are other ways to disable logging that don't require modifications to the code. I don't think this needs merging tbh.

Closed then.


Please provide a requirements.txt for succussfully running the examples.

Many thanks!

We have released Towhee 1.1.1, it uses the correct version of pydantic

I am using towhee=1.1.1, the example of Reverse Image Search still has issue.
I tried this notebook, everything works well except the step of # Display search results with images, no need for implementation
Here is the error details. I think it's still related to pydantic. I have checked the version of it, I do use pydantic =1.10.10
Cell In[36], line 13
      9         imgs.append(Image(cv2.imread(p), 'BGR'))
     10     return imgs
     12 p_search_img = (
---> 13     p_search_pre.map('pred', 'pred images', read_images)
     14                 .output('img', 'pred images')
     15 )
     16 DataCollection(p_search_img('test/goldfish/*.JPEG')).show()

File [~/Documents/self-project/milvus/venv/lib/python3.10/site-packages/towhee/runtime/pipeline.py:123](https://file+.vscode-resource.vscode-cdn.net/Users/REA_bin.wang/Documents/self-project/milvus/~/Documents/self-project/milvus/venv/lib/python3.10/site-packages/towhee/runtime/pipeline.py:123), in Pipeline.map(self, input_schema, output_schema, fn, config)
    104 def map(self, input_schema, output_schema, fn, config=None) -> 'Pipeline':
    105     """
    106     One to one map of function on inputs.
    107
   (...)
    121         [1, 2]
    122     """
--> 123     output_schema = self._check_schema(output_schema)
    124     input_schema = self._check_schema(input_schema)
    126     uid = uuid.uuid4().hex

File [~/Documents/self-project/milvus/venv/lib/python3.10/site-packages/towhee/runtime/pipeline.py:517](https://file+.vscode-resource.vscode-cdn.net/Users/REA_bin.wang/Documents/self-project/milvus/~/Documents/self-project/milvus/venv/lib/python3.10/site-packages/towhee/runtime/pipeline.py:517), in Pipeline._check_schema(schema)
    515 @staticmethod
...
File [~/Documents/self-project/milvus/venv/lib/python3.10/site-packages/pydantic/main.py:341](https://file+.vscode-resource.vscode-cdn.net/Users/REA_bin.wang/Documents/self-project/milvus/~/Documents/self-project/milvus/venv/lib/python3.10/site-packages/pydantic/main.py:341), in pydantic.main.BaseModel.__init__()

ValidationError: 1 validation error for TupleForm
schema_data -> 0
  string does not match regex "^[a-z][a-z0-9_]*$" (type=value_error.str.regex; pattern=^[a-z][a-z0-9_]*$)

I am a beginner of towhee, it seem the previous error is caused by those lines
p_search_img = (
    p_search_pre.map('pred', 'pred images', read_images)
                .output('img', 'pred images')

the schema does not support space, replace pred images with pred_images could solve this issue

Sorry for raising this issue, I am a beginner of towhee. It seem the previous error is caused by those lines
p_search_img = (
    p_search_pre.map('pred', 'pred images', read_images)
                .output('img', 'pred images')

the schema does not support space, replace pred images with pred_images could solve this issue

Fixed by https://github.com/towhee-io/examples/pull/220


Slow boot time due to repeated system image loading

RancherOS Version: (ros os version)
v1.4.0
Where are you running RancherOS? (docker-machine, AWS, GCE, baremetal, etc.)
vSphere
2 vCPU / 2048 MB
Expected:
Boot time ~ 60 seconds
Actual:
vmware ISO: 90 seconds boot time
vmware disk install: 70 seconds boot time
Most of the boot time is spent loading the system images from images.tar. This is done 3 times during bootstrapping, cloud-init, and init phase. When booting from vmware ISO a total of 50 seconds was spent loading the images (see below).
It looks like the first two boot phases require only 1 out of the 9 images included in images.tar.
To reduce boot time, we could split images.tar in multiple archives to only load those that are required in each phase.
--- dmesg ---
[Fri Sep  7 19:09:53 2018] Linux version 4.14.32-rancher2 (root@148079e86502) (gcc version 7.3.0 (GCC)) #1 SMP Fri May 11 11:30:31 UTC 2018
(...)
[    0.605837] Unpacking initramfs...
[   22.892150] Freeing initrd memory: 80672K
(...)
[   24.807893] ip_tables: (C) 2000-2006 Netfilter Core Team
[   44.501799] cgroup: system-docker-r (380) created nested cgroup for controller "memory" which has incomplete hierarchy support. Nested cgroups may change behavior in the future.
(...)
[   48.417880] EXT4-fs (sda): mounted filesystem with ordered data mode. Opts: (null)
[   60.508546] udevd[633]: starting version 3.2.5
(...)
[   65.731908] IPv6: ADDRCONF(NETDEV_UP): docker-sys: link is not ready
[   88.870509] udevd[20]: starting version 3.2.5
(...)
[Fri Sep  7 19:11:24 2018] vmxnet3 0000:0b:00.0 eth0: NIC Link is Up 10000 Mbps

--- init.log: 30 seconds spent loading images ---
(...)
time="2018-09-07T19:10:17Z" level=info msg="[1/2] Starting bootstrap loadImages"
time="2018-09-07T19:10:17Z" level=debug msg="Looking for images at /usr/share/ros"
time="2018-09-07T19:10:17Z" level=debug msg="Found images.tar"
time="2018-09-07T19:10:17Z" level=info msg="Waiting for Docker at unix:///var/run/system-docker.sock"
time="2018-09-07T19:10:18Z" level=info msg="Connected to Docker at unix:///var/run/system-docker.sock"
time="2018-09-07T19:10:18Z" level=info msg="Loading images from /usr/share/ros/images.tar"
time="2018-09-07T19:10:37Z" level=info msg="Done loading images from /usr/share/ros/images.tar"
(...)
time="2018-09-07T19:10:41Z" level=info msg="[1/2] Starting cloudinit loadImages"
time="2018-09-07T19:10:41Z" level=debug msg="Looking for images at /usr/share/ros"
time="2018-09-07T19:10:41Z" level=debug msg="Found images.tar"
time="2018-09-07T19:10:41Z" level=info msg="Waiting for Docker at unix:///var/run/system-docker.sock"
time="2018-09-07T19:10:43Z" level=info msg="Connected to Docker at unix:///var/run/system-docker.sock"
time="2018-09-07T19:10:43Z" level=info msg="Loading images from /usr/share/ros/images.tar"
time="2018-09-07T19:10:53Z" level=info msg="Done loading images from /usr/share/ros/images.tar"
(...)

--- ros-sysinit.log: 20 seconds spent loading images ---
(...)
time="2018-09-07T19:10:57Z" level=info msg="[1/4] Starting loadImages"
time="2018-09-07T19:10:57Z" level=debug msg="Looking for images at /usr/share/ros"
time="2018-09-07T19:10:57Z" level=debug msg="Found images.tar"
time="2018-09-07T19:10:59Z" level=info msg="Loading images from /usr/share/ros/images.tar"
time="2018-09-07T19:11:20Z" level=info msg="Done loading images from /usr/share/ros/images.tar"
(...)
time="2018-09-07T19:11:29Z" level=info msg="RancherOS 289ff05 started"

Mitigated by https://github.com/rancher/os/pull/2474

Bare metal installed on ssd unpacking initramfs needs 39,1 seconds and I have to wait for a full boot 70 seconds.
Don't know how long that problem exists. It's for a long time... That's why I moved away.
Linuxkit docker boots really fast and that should be possible with RancherOS too...
If possible the preload images shouldn't included to the (installed version of) initramfs of RancherOS, because the images should be loaded from disk.

Before we can officially release 1.5, anybody who hit this problem can try this iso.
http://releases.rancher.com/os/test/rancheros-master.iso

Bare metal install boot time reduced from 70 seconds to 28,5 seconds.
But maybe a new error appeared during boot (I see no problems after boot).
[28.044800] system-dockerd[1481]: segfault at 8 ip<PHONE_NUMBER>542436 sp 000000c421587308 error 4 in system-dockerd[400000+1485000]

Maybe for installed version the image preload could be removed (overlay partition?) or moved out of the initrd to reduce the boot time again?

@pwFoo Thanks for looking into this.
About that kernel segfault, I have been tracking, but no progress, but it seems to have no effect.
In the early versions, we didn't put the system image in the initrd, so it booted up very quickly, but if the user needs to use the full RancherOS, they still need to manually download the images and install them. Especially when the user boots up using iPXE mode, the built-in system images cannot be removed.
From the user experience, we have to put it in the initrd.
But there is indeed a point to improve, when the system-docker loads the image, different image compression methods may affect the load time. We also need to do a lot of testing.
Zstd may be one of the options.

Created a new issue to track your idea.


NPM: Raise git not reachable for ssh://git

Relying on SharedHelpers.with_git_configured.. to rewrite<EMAIL_ADDRESS>to https:// so don't need to guard against ssh:// when raising error
about unreachable git repo.

@greysteil haven't tested if works when we have access, would be good to get some help with tests

FYI, I have to roll back the --global -> --system change, but it looks like SSL conversion is still working (it's just the error message that was causing problems).
The reason for the rollback is that Dependabot isn't a privileged user when it runs in the dependabot-updater, so can't set --system. Still, having that github dependency test let me easily check that everything still worked. 🎉


Various fixes.

Updated Makefile to set image version based on git tag.
Fixed issue 12
Added missing file entrypoint.sh

Rejecting the pull request \so I can sign the commits.


The first argument of parse method could be of type Node, not HTMLElement

The first argument of parse() (and parseNode(), grabAllTextNodes()) could be Node to allow to work with Element and also with DocumentFragment. Only the childNodes property (of Node) is accessed on the argument, I think.

do you have an actual issue or this is about the comments within the code ?

No issue, only a request, my mistake I should have put a request, I apologize.

only a request to change something

if it's just the comment you would like to change, why don't you file a PR directly?
This is super low priority to me, sorry.


[BEAM-8335] Final PR to merge the InteractiveBeam feature branch

Final PR to merge everything from the InteractiveBeam feature branch
consumer_tracking_pipeline_visitor.py:

This has the assumption that the pipeline proto is in topological order. The PipelineProto has no guarantee for this. This changes a key part to a set instead of a list.

consumer_tracking_pipeline_visitor_test.py:

This adds a test that fails if the class has an order guarantee.

test_stream_impl.py

Adds an endpoint to a forgotten constructor.

background_caching_job.py

Fixes grammar mistake

background_caching_job_test.py

Adds the StreamingCache to the test

streaming_cache.py:

Small cleanup
The values in the cache can either be protos or not in the case of testing.

pcoll_visualization:

Adds ability to read from a StreamingCache
Refactors _to_dataframe implementation with elements_to_df

pipeline_graph.py:

Add a comment

interactive_beam.py:

Add the collect/head methods which take in a PCollection and outputs a Dataframe representation
Adds logic to not recompute PCollections already in the cache

interactive_runner.py

Adds the TestStreamServiceController
Adds ability for the result.get method to read from the StreamingCache

interactive_runner_test.py

Add a small integration test for the new DataFrame

capture_control.py

formatting

pipeline_fragment.py

Add logic to stop pruning the pipeline fragment if it hits a TestStream

pipeline_instrument.py

Change dictionary usage to WindowedValueHolder

pipeline_instrumnet_test.py

formatting

pipeline_assertion.py:

formatting

utils.py:

Small refactor, moving the to_element_list from the pcoll_visualization and the PipelineResult.get methods to here.

Thank you for your contribution! Follow this checklist to help us incorporate your contribution quickly and easily:

[ ] Choose reviewer(s) and mention them in a comment (R: @username).
[ ] Format the pull request title like [BEAM-XXX] Fixes bug in ApproximateQuantiles, where you replace BEAM-XXX with the appropriate JIRA issue, if applicable. This will automatically link the pull request to the issue.
[ ] Update CHANGES.md with noteworthy changes.
[ ] If this contribution is large, please file an Apache Individual Contributor License Agreement.

See the Contributor Guide for more tips on how to make review process smoother.
Post-Commit Tests Status (on master branch)

Lang
SDK
Apex
Dataflow
Flink
Gearpump
Samza
Spark

Go

---
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---
---

Java

Python

---

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---

XLang
---
---
---

---
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Pre-Commit Tests Status (on master branch)

---
Java
Python
Go
Website

Non-portable

Portable
---

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---

See .test-infra/jenkins/README for trigger phrase, status and link of all Jenkins jobs.

retest this please

retest this please

retest this please

Retest this please

Retest this please


Store should implement lookuJobTimestamp* methods

com.kazurayam.materialstore.store.Store should implement the following methods
    JobTimestamp lookupJobTimestampLatest(JobName jobName)

    JobTimestamp lookupJobTimestamp2ndLatest(JobName jobName)

    JobTimestamp lookupJobTimestampPriorTo(JobName jobName, JobTimestamp jobTimestamp)

    List<JobTimestamp> lookupAllJobTimestampsPriorTo(JobName jobName, JobTimestamp jobTimestamp)

JobTimestamp class should implement plus/minus operation.

done


o3d303: Lib: XMLRPC Timeout - can you `ping' the sensor?

I am trying to connect to the O3D303 Sensor using debian 9 and ROS Melodic.When I run catkin_make  run_tests, l get these errors:

Here is the failed  trying:

mway@mway:~$ avahi-browse -arp 2>/dev/null | grep  ^=.*O3D.*Workstation
=;enp1s0;IPv4;O3D3XX-41-8D-BA-7\032\09100\05802\05801\05841\0588d\058ba\093;Workstation;local;O3D3XX-41-8D-BA-7.local;<IP_ADDRESS>;9;

@zener90818 its seems your  host system has ip <IP_ADDRESS> which i think is conflicting  with the default ip of the device and hence  device is taking a local-link address.  Quick first solution would be to change the ip of the your system to 192.168.0.xx where xx takes other values than 69. checkout our troubleshoot guide for connection issues.

@inbangsa thanks for reply. i change the ip to <IP_ADDRESS>, it doesn't seem to work.  Did i change the ip of the device in the right way?

Hey @zener90818 it seems like you've changed the IP address of your camera -- @inbangsa suggested you change the IP address assigned to your host (the enp1s0 interface). Nevertheless, this should work but you have to set an environment variable.
Add the following to your .bashrc and logout/login:
export IFM3D_IP=<IP_ADDRESS>

Alternatively if you want to avoid the environment variable you can pass the IP directly to the ROS node and the ifm3d tool:
$ ifm3d --ip=<IP_ADDRESS> ls
$ roslaunch ifm3d camera.launch ip:=<IP_ADDRESS>

Finally just an FYI -- the ifm3d library as well as the ROS package are available through the ROS distro:
$ sudo apt install ros-melodic-ifm3d

@zener90818 how you changed the ip of the device ?  or you changed the ip of the host system ?
I assume your camera  is at ip address of ``<IP_ADDRESS>and your host system is at <IP_ADDRESS>``` then as per troubleshoot guide please follow this steps  and then try
$ ifm3d --ip=<IP_ADDRESS> ls

@inbangsa @theseankelly Thanks a lot. the problem is solved.

Hi @zener90818 , I am trying to connect to O3R device and I am facing similar issue with respect to connection. I have followed the steps described in this discussion and I have set the IP address of my pc. PC IP settings

I tried connecting to the sensor using the default IP address, but i was not successful. And also I am not able to retrieve the ip address of the camera using the troubleshooting guidelines provided here.
Any suggestions would be very helpful. Thanks in advance.


flutter drive device identifier

Use case
We run more than one android emulator and have now the problem, that we can't select the correct AVD with the flutter -d '...' drive command.
Could you please change the line
https://github.com/flutter/flutter/blob/master/packages/flutter_tools/lib/src/android/android_device.dart#L760
to
    info['model'] = cleanAdbDeviceName(info['model']) + ' ' + deviceID;

Than it is possible to select the emulators more specific.
Look at https://github.com/flutter/flutter-intellij/issues/1244 to get an idea of the details (if I am unclear).

Thanks for the feedback, @ireinhart. Curious: is there a reason why you're not comfortable submitting this as a pull request? Thank you again!

Ok, thanks for your hint. I found a better solution and create a PR.
See https://github.com/flutter/flutter/pull/38296


Validator block proposal missing

Waiting 259 days and missed a block proposal today...
I have seen multiple subreddit post with the same error lately and wondering if there is not a bug or something wrong with mev-boost.
In my case I missed 2,29 ethereum tips, so it is a lot of money... and never missed a block proposal before enabling mev-boost.
Have you any advice or configuration change to avoid this error in the future?
Thanks,
Environment :
Rocky Linux 8.8
Lighthouse 4.1.0
Geth 1.11.6
Mev-boost 1.5.0
Mev-boost service configuration :
[Unit]
Description=Maximal Extractable Value Boost (Mainnet)
Wants=network-online.target
After=network-online.target
[Service]
Type=simple
User=mevboost
Group=mevboost
Restart=always
RestartSec=5
ExecStart=/usr/local/bin/mev-boost
-mainnet
-relay-check
-relays https://0xac6e77dfe25ecd6110b8e780608cce0dab71fdd5ebea22a16c0205200f2f8e2e3ad3b71d3499c54ad14d6c21b41a37ae@boost-relay.flashbots.net,https://0xa1559ace749633b997cb3fdacffb890aeebdb0f5a3
<EMAIL_ADDRESS>[Install]
WantedBy=default.target
Internet connection speed :
ISP: Free SAS
Idle Latency:    15.45 ms   (jitter: 0.33ms, low: 15.24ms, high: 16.00ms)
Download:   935.23 Mbps (data used: 1.6 GB)
44.42 ms   (jitter: 24.14ms, low: 15.42ms, high: 362.09ms)
Upload:   626.76 Mbps (data used: 1.1 GB)
16.18 ms   (jitter: 2.11ms, low: 15.16ms, high: 38.19ms)
Packet Loss:     0.0%
Mev-boost logs :
May 21 17:57:11 ethstake1.local mev-boost[1139609]: time="2023-05-21T17:57:11+02:00" level=info msg="http: POST /eth/v1/builder/validators 200" duration=0.099271 method=POST path=/eth/v1/builder/va
lidators status=200 version=v1.5.0
May 21 18:00:38 ethstake1.local mev-boost[1139609]: time="2023-05-21T18:00:38+02:00" level=info msg="best bid" blockHash=0x55b9bbadb92dd4be4d10a0ffb2affcbed4e6056a6ba6372d7bcaf35799e413de blockNumb
er=17308790 method=getHeader parentHash=0x467978a22952fcc82d23ef4a1b3ba75b637a86fb6bd35934b8f5e7bcad1090b9 pubkey=0xb28b4c8805056122b8793da25c9d1ce53a04e81bbc40f51d06ac78d52d1eae32b24ea946208897de6
cd6524ec5c2eead<EMAIL_ADDRESS>slot=6488401 txRoot=0x392c260d78b53f8f8038
2c2cb70565a178f0dad552563f8aaefd8631445ab443 value=2.299343265057725978 version=v1.5.0
May 21 18:00:38 ethstake1.local mev-boost[1139609]: time="2023-05-21T18:00:38+02:00" level=info msg="http: GET /eth/v1/builder/header/6488401/0x467978a22952fcc82d23ef4a1b3ba75b637a86fb6bd35934b8f5e
7bcad1090b9/0xb28b4c8805056122b8793da25c9d1ce53a04e81bbc40f51d06ac78d52d1eae32b24ea946208897de6cd6524ec5c2eead 200" duration=0.231985 method=GET path=/eth/v1/builder/header/6488401/0x467978a22952fc
c82d23ef4a1b3ba75b637a86fb6bd35934b8f5e7bcad1090b9/0xb28b4c8805056122b8793da25c9d1ce53a04e81bbc40f51d06ac78d52d1eae32b24ea946208897de6cd6524ec5c2eead status=200 version=v1.5.0
May 21 18:00:39 ethstake1.local mev-boost[1139609]: time="2023-05-21T18:00:39+02:00" level=warning msg="error making request to relay, retrying" blockHash=0x55b9bbadb92dd4be4d10a0ffb2affcbed4e6056a
6ba6372d7bcaf35799e413de error="HTTP error response: 400 / {"code":400,"message":"sent too late - 4049 ms into slot"}\n" method=getPayload parentHash=0x467978a22952fcc82d23ef4a1b3ba75b637a86f
b6bd35934b8f5e7bcad1090b9 slot=6488401 url="https://relay.ultrasound.money/eth/v1/builder/blinded_blocks" version=v1.5.0
May 21 18:00:39 ethstake1.local mev-boost[1139609]: time="2023-05-21T18:00:39+02:00" level=warning msg="error making request to relay, retrying" blockHash=0x55b9bbadb92dd4be4d10a0ffb2affcbed4e6056a
6ba6372d7bcaf35799e413de error="HTTP error response: 400 / {"code":400,"message":"sent too late - 4251 ms into slot"}\n" method=getPayload parentHash=0x467978a22952fcc82d23ef4a1b3ba75b637a86f
b6bd35934b8f5e7bcad1090b9 slot=6488401 url="https://relay.ultrasound.money/eth/v1/builder/blinded_blocks" version=v1.5.0
May 21 18:00:39 ethstake1.local mev-boost[1139609]: time="2023-05-21T18:00:39+02:00" level=warning msg="error making request to relay, retrying" blockHash=0x55b9bbadb92dd4be4d10a0ffb2affcbed4e6056a
6ba6372d7bcaf35799e413de error="HTTP error response: 400 / {"code":400,"message":"sent too late - 4436 ms into slot"}\n" method=getPayload parentHash=0x467978a22952fcc82d23ef4a1b3ba75b637a86f
b6bd35934b8f5e7bcad1090b9 slot=6488401 url="https://relay.ultrasound.money/eth/v1/builder/blinded_blocks" version=v1.5.0
May 21 18:00:39 ethstake1.local mev-boost[1139609]: time="2023-05-21T18:00:39+02:00" level=warning msg="error making request to relay, retrying" blockHash=0x55b9bbadb92dd4be4d10a0ffb2affcbed4e6056a
6ba6372d7bcaf35799e413de error="HTTP error response: 400 / {"code":400,"message":"sent too late - 4620 ms into slot"}\n" method=getPayload parentHash=0x467978a22952fcc82d23ef4a1b3ba75b637a86f
b6bd35934b8f5e7bcad1090b9 slot=6488401 url="https://relay.ultrasound.money/eth/v1/builder/blinded_blocks" version=v1.5.0
May 21 18:00:39 ethstake1.local mev-boost[1139609]: time="2023-05-21T18:00:39+02:00" level=warning msg="error making request to relay, retrying" blockHash=0x55b9bbadb92dd4be4d10a0ffb2affcbed4e6056a
6ba6372d7bcaf35799e413de error="HTTP error response: 400 / {"code":400,"message":"sent too late - 4809 ms into slot"}\n" method=getPayload parentHash=0x467978a22952fcc82d23ef4a1b3ba75b637a86f
b6bd35934b8f5e7bcad1090b9 slot=6488401 url="https://relay.ultrasound.money/eth/v1/builder/blinded_blocks" version=v1.5.0
May 21 18:00:39 ethstake1.local mev-boost[1139609]: time="2023-05-21T18:00:39+02:00" level=error msg="error making request to relay" blockHash=0x55b9bbadb92dd4be4d10a0ffb2affcbed4e6056a6ba6372d7bca
f35799e413de error="max retries exceeded" method=getPayload parentHash=0x467978a22952fcc82d23ef4a1b3ba75b637a86fb6bd35934b8f5e7bcad1090b9 slot=6488401 url="https://relay.ultrasound.money/eth/v1/bui
lder/blinded_blocks" version=v1.5.0
May 21 18:00:39 ethstake1.local mev-boost[1139609]: time="2023-05-21T18:00:39+02:00" level=error msg="no payload received from relay!" blockHash=0x55b9bbadb92dd4be4d10a0ffb2affcbed4e6056a6ba6372d7b
caf35799e413de method=getPayload parentHash=0x467978a22952fcc82d23ef4a1b3ba75b637a86fb6bd35934b8f5e7bcad1090b9 relays="https://0xa1559ace749633b997cb3fdacffb890aeebdb0f5a3b6aaa7eeeaf1a38af0a8fe88b9
<EMAIL_ADDRESS>slot=6488401 version=v1.5.0
May 21 18:00:39 ethstake1.local mev-boost[1139609]: time="2023-05-21T18:00:39+02:00" level=info msg="http: POST /eth/v1/builder/blinded_blocks 502" duration=0.973313 method=POST path=/eth/v1/builde
r/blinded_blocks status=502 version=v1.5.0
May 21 18:03:35 ethstake1.local mev-boost[1139609]: time="2023-05-21T18:03:35+02:00" level=info msg="http: POST /eth/v1/builder/validators 200" duration=0.104025 method=POST path=/eth/v1/builder/va
lidators status=200 version=v1.5.0
Lighthouse validator logs :
May 21 18:00:29 ethstake1.local lighthouse[623535]: May 21 16:00:29.001 INFO Connected to beacon node(s)             synced: 1, available: 1, total: 1, service: notifier
May 21 18:00:29 ethstake1.local lighthouse[623535]: May 21 16:00:29.001 INFO All validators active                   slot: 6488400, epoch: 202762, total_validators: 1, active_validators: 1, current
_epoch_proposers: 1, service: notifier
May 21 18:00:35 ethstake1.local lighthouse[623535]: May 21 16:00:35.003 INFO Requesting unsigned block               slot: 6488401, service: block
May 21 18:00:38 ethstake1.local lighthouse[623535]: May 21 16:00:38.935 INFO Received unsigned block                 slot: 6488401, service: block
May 21 18:00:38 ethstake1.local lighthouse[623535]: May 21 16:00:38.948 INFO Publishing signed block                 signing_time_ms: 11, slot: 6488401, service: block
May 21 18:00:39 ethstake1.local lighthouse[623535]: May 21 16:00:39.946 ERRO Error whilst producing a blinded block, cannot fallback because the block was signed, error: "Some endpoints failed, num
_failed: 1 http://localhost:5052/ => RequestFailed(Irrecoverable("Error from beacon node when publishing block: ServerMessage(ErrorMessage { code: 500, message: \"INTERNAL_SERVER_ERROR: Blind bl
ock proposal failed: Builder(ServerMessage(ErrorMessage { code: 502, message: \\\"no successful relay response\\\", stacktraces: [] }))\", stacktraces: [] })"))", service: block
May 21 18:00:39 ethstake1.local lighthouse[623535]: May 21 16:00:39.947 CRIT Error whilst producing block            message: Irrecoverable("Some endpoints failed, num_failed: 1 http://localhost:50
52/ => RequestFailed(Irrecoverable("Error from beacon node when publishing block: ServerMessage(ErrorMessage { code: 500, message: \"INTERNAL_SERVER_ERROR: Blind block proposal failed: Builder(S
erverMessage(ErrorMessage { code: 502, message: \\\"no successful relay response\\\", stacktraces: [] }))\", stacktraces: [] })"))"), service: block
...skipping 1 line
May 21 18:00:41 ethstake1.local lighthouse[623535]: May 21 16:00:41.000 INFO All validators active                   slot: 6488401, epoch: 202762, total_validators: 1, active_validators: 1, current
_epoch_proposers: 1, service: notifier
May 21 18:00:46 ethstake1.local lighthouse[623535]: May 21 16:00:46.036 INFO Connected to beacon node                endpoint: http://localhost:5052/, version: Lighthouse/v4.1.0-693886b/x86_64-linu
x
Lighthouse beacon logs :
May 21 18:00:17 ethstake1.local lighthouse[623513]: May 21 16:00:17.005 INFO Synced                                  slot: 6488399, block: 0x4319…e99b, epoch: 202762, finalized_epoch: 202760, final
ized_root: 0xb0d8…2d99, exec_hash: 0x4846…9f7f (verified), peers: 82, service: slot_notifier
May 21 18:00:23 ethstake1.local lighthouse[623513]: May 21 16:00:23.027 INFO Prepared beacon proposer                parent_root: 0x431942805b0f6e5d5118203514b2c66f5bff70c51c1ed0d114b7aa13ec45e99b,
validator: 12610, prepare_slot: 6488401, service: beacon
May 21 18:00:25 ethstake1.local lighthouse[623513]: May 21 16:00:25.357 INFO New block received                      root: 0x3ee4a8c68a4d02f13567e7e399d8c67f0ef4659ca8b52e0cd69382ee8da5912d, slot:
6488400
May 21 18:00:25 ethstake1.local lighthouse[623513]: May 21 16:00:25.935 INFO Prepared beacon proposer                parent_root: 0x3ee4a8c68a4d02f13567e7e399d8c67f0ef4659ca8b52e0cd69382ee8da5912d,
validator: 12610, prepare_slot: 6488401, service: beacon
May 21 18:00:29 ethstake1.local lighthouse[623513]: May 21 16:00:29.002 INFO Synced                                  slot: 6488400, block: 0x3ee4…912d, epoch: 202762, finalized_epoch: 202760, final
ized_root: 0xb0d8…2d99, exec_hash: 0x4679…90b9 (verified), peers: 88, service: slot_notifier
May 21 18:00:38 ethstake1.local lighthouse[623513]: May 21 16:00:38.545 INFO Requesting blinded header from connected builder, parent_hash: 0x467978a22952fcc82d23ef4a1b3ba75b637a86fb6bd35934b8f5e7b
cad1090b9, pubkey: 0xb28b4c8805056122b8793da25c9d1ce53a04e81bbc40f51d06ac78d52d1eae32b24ea946208897de6cd6524ec5c2eead, slot: Slot(6488401), service: exec
May 21 18:00:38 ethstake1.local lighthouse[623513]: May 21 16:00:38.781 INFO Requested blinded execution payload     parent_hash: 0x467978a22952fcc82d23ef4a1b3ba75b637a86fb6bd35934b8f5e7bcad1090b9,
local_response_ms: 82, local_fee_recipient: 0x6a4f1fe4c77ecda51735805507b997894b5fb982, relay_response_ms: 236, relay_fee_recipient: 0x1f9090aae28b8a3dceadf281b0f12828e676c326, service: exec
May 21 18:00:38 ethstake1.local lighthouse[623513]: May 21 16:00:38.781 INFO Received local and builder payloads     parent_hash: 0x467978a22952fcc82d23ef4a1b3ba75b637a86fb6bd35934b8f5e7bcad1090b9,
local_block_hash: 0x1a0345e6c0791ddeaa1aa4964567a2e4e0d93e1ec5e7ed5b387f73830af916c1, relay_block_hash: 0x55b9bbadb92dd4be4d10a0ffb2affcbed4e6056a6ba6372d7bcaf35799e413de, service: exec
May 21 18:00:38 ethstake1.local lighthouse[623513]: May 21 16:00:38.970 ERRO Block broadcast was delayed             root: 0xd268499d82495f285399326f38fa29349ddcc70901eccdab27c11546971a3cfd, slot:
6488401, delay_ms: 3969, provenance: builder, msg: system may be overloaded, block may be orphaned
May 21 18:00:39 ethstake1.local lighthouse[623513]: May 21 16:00:39.944 WARN Builder failed to reveal payload        parent_hash: "0x4679…90b9", block_root: 0xd268499d82495f285399326f38fa29349ddcc7
0901eccdab27c11546971a3cfd, relay_response_ms: 974, error: Builder(ServerMessage(ErrorMessage { code: 502, message: "no successful relay response", stacktraces: [] })), info: this is common behavio
ur for some builders and may not indicate an issue, service: exec
May 21 18:00:39 ethstake1.local lighthouse[623513]: May 21 16:00:39.945 WARN Error processing HTTP API request       method: POST, path: /eth/v1/beacon/blinded_blocks, status: 500 Internal Server E
rror, elapsed: 995.172056ms
May 21 18:00:41 ethstake1.local lighthouse[623513]: May 21 16:00:41.001 INFO Synced                                  slot: 6488401, block:    …  empty, epoch: 202762, finalized_epoch: 202760, final
ized_root: 0xb0d8…2d99, exec_hash: 0x4679…90b9 (verified), peers: 88, service: slot_notifier
Geth logs :
May 21 17:59:49 ethstake1.local geth[623818]: INFO [05-21|17:59:49.940] Chain head was updated                   number=17,308,786 hash=0dacb9..08672e root=313d48..6a94d0 elapsed=5.071822ms
May 21 18:00:01 ethstake1.local geth[623818]: INFO [05-21|18:00:01.425] Imported new potential chain segment     number=17,308,787 hash=ac02ef..f85382 blocks=1 txs=177  mgas=20.831 elapsed=176.636m
s     mgasps=117.931 dirty=1022.10MiB
May 21 18:00:01 ethstake1.local geth[623818]: INFO [05-21|18:00:01.630] Chain head was updated                   number=17,308,787 hash=ac02ef..f85382 root=440238..373742 elapsed=5.892012ms
May 21 18:00:13 ethstake1.local geth[623818]: INFO [05-21|18:00:13.544] Imported new potential chain segment     number=17,308,788 hash=4846bc..b39f7f blocks=1 txs=563  mgas=28.498 elapsed=327.373m
s     mgasps=87.049  dirty=1022.63MiB
May 21 18:00:13 ethstake1.local geth[623818]: INFO [05-21|18:00:13.817] Chain head was updated                   number=17,308,788 hash=4846bc..b39f7f root=2af664..ef4de5 elapsed=12.231796ms
May 21 18:00:25 ethstake1.local geth[623818]: INFO [05-21|18:00:25.698] Imported new potential chain segment     number=17,308,789 hash=467978..1090b9 blocks=1 txs=198  mgas=17.127 elapsed=156.770m
s     mgasps=109.246 dirty=1022.57MiB
May 21 18:00:25 ethstake1.local geth[623818]: INFO [05-21|18:00:25.930] Chain head was updated                   number=17,308,789 hash=467978..1090b9 root=b0daae..501e67 elapsed=8.833415ms
May 21 18:00:31 ethstake1.local geth[623818]: INFO [05-21|18:00:31.258] Starting work on payload                 id=0xf676267490e34227
May 21 18:00:31 ethstake1.local geth[623818]: INFO [05-21|18:00:31.879] Updated payload                          id=0xf676267490e34227 number=17,308,790 hash=16a816..7192dc txs=146  gas=15,356,722
fees=0.08419713087 root=75081d..8dbeb8 elapsed=620.746ms
May 21 18:00:33 ethstake1.local geth[623818]: INFO [05-21|18:00:33.968] Updated payload                          id=0xf676267490e34227 number=17,308,790 hash=f0aeff..8504c1 txs=181  gas=18,046,689
fees=0.1565930646  root=1bdaf0..0cd0a3 elapsed=87.547ms
May 21 18:00:36 ethstake1.local geth[623818]: INFO [05-21|18:00:36.081] Updated payload                          id=0xf676267490e34227 number=17,308,790 hash=6ba6a9..284e5d txs=202  gas=20,337,955
fees=0.1592364734  root=c1c907..17cf9c elapsed=112.834ms
May 21 18:00:38 ethstake1.local geth[623818]: INFO [05-21|18:00:38.317] Updated payload                          id=0xf676267490e34227 number=17,308,790 hash=1a0345..f916c1 txs=226  gas=23,659,356
fees=0.1606250069  root=47b483..16056b elapsed=235.263ms
May 21 18:00:38 ethstake1.local geth[623818]: INFO [05-21|18:00:38.554] Stopping work on payload                 id=0xf676267490e34227 reason=delivery
May 21 18:00:50 ethstake1.local geth[623818]: INFO [05-21|18:00:50.354] Imported new potential chain segment     number=17,308,790 hash=35cb46..e134b9 blocks=1 txs=300  mgas=29.986 elapsed=364.200m
s     mgasps=82.335  dirty=1022.46MiB
May 21 18:00:50 ethstake1.local geth[623818]: INFO [05-21|18:00:50.795] Chain head was updated                   number=17,308,790 hash=35cb46..e134b9 root=b4079f..d261a8 elapsed=8.559389ms
May 21 18:01:01 ethstake1.local geth[623818]: INFO [05-21|18:01:01.470] Imported new potential chain segment     number=17,308,791 hash=f63ab5..2de1f0 blocks=1 txs=216  mgas=19.394 elapsed=389.033m
s     mgasps=49.853  dirty=1022.53MiB
May 21 18:01:02 ethstake1.local geth[623818]: INFO [05-21|18:01:02.209] Chain head was updated                   number=17,308,791 hash=f63ab5..2de1f0 root=b15e1b..16bba7 elapsed=39.005056ms
May 21 18:01:12 ethstake1.local geth[623818]: INFO [05-21|18:01:12.898] Imported new potential chain segment     number=17,308,792 hash=c76cdd..18a8da blocks=1 txs=154  mgas=10.917 elapsed=137.189m
s     mgasps=79.577  dirty=1021.79MiB
May 21 18:01:13 ethstake1.local geth[623818]: INFO [05-21|18:01:13.074] Chain head was updated                   number=17,308,792 hash=c76cdd..18a8da root=ef1835..5eb9e5 elapsed=4.151622ms
May 21 18:01:24 ethstake1.local geth[623818]: INFO [05-21|18:01:24.612] Imported new potential chain segment     number=17,308,793 hash=6e6104..1de798 blocks=1 txs=153  mgas=11.243 elapsed=200.568m
s     mgasps=56.056  dirty=1022.12MiB
May 21 18:01:24 ethstake1.local geth[623818]: INFO [05-21|18:01:24.913] Chain head was updated                   number=17,308,793 hash=6e6104..1de798 root=6b1e67..12ed38 elapsed=4.385776ms
May 21 18:01:38 ethstake1.local geth[623818]: INFO [05-21|18:01:38.081] Imported new potential chain segment     number=17,308,794 hash=3af4a3..f9825e blocks=1 txs=158  mgas=17.949 elapsed=182.529m
s     mgasps=98.334  dirty=1022.10MiB
May 21 18:01:38 ethstake1.local geth[623818]: INFO [05-21|18:01:38.309] Chain head was updated                   number=17,308,794 hash=3af4a3..f9825e root=8d4c35..d4e67a elapsed=4.736565ms
May 21 18:01:49 ethstake1.local geth[623818]: INFO [05-21|18:01:49.411] Imported new potential chain segment     number=17,308,795 hash=d652fe..40f949 blocks=1 txs=140  mgas=12.028 elapsed=138.475m
s     mgasps=86.862  dirty=1021.97MiB
May 21 18:01:49 ethstake1.local geth[623818]: INFO [05-21|18:01:49.552] Chain head was updated                   number=17,308,795 hash=d652fe..40f949 root=5e3984..f22134 elapsed=3.158046ms
May 21 18:02:00 ethstake1.local geth[623818]: INFO [05-21|18:02:00.844] Imported new potential chain segment     number=17,308,796 hash=ca6a8c..499fd7 blocks=1 txs=146  mgas=11.937 elapsed=197.200m
s     mgasps=60.532  dirty=1021.86MiB

The same thing that happened to me has taken you more than 4 seconds to send and it has been rejected. https://github.com/flashbots/mev-boost/issues/516
I have an issue open in prysm too, they seem to have released a flag to build in parallel, I don't know if their consensus client has worked on that.
Sorry my bad english.

Same issue as #516 -- network issues between proposer and ultrasound relay, making the getPayload request arrive too late at the relay and getting rejected (slot would have been missed anyway because any block after 4 seconds is rejected by the beacon network / CL clients).
For reference -- this was for slot 6,488,401 with start timestamp 1,684,684,835 (which is Sun May 21 2023 16:00:35 GMT+0000). getHeader response arrived at :38, which is already 3 seconds into the slot.
We may want to consider mev-boost not to return a header if it's past 3 seconds into the slot already 🤔  cc/ @ralexstokes
@michaelneuder @jtraglia @terencechain
Closing this issue now, as there's nothing else to do here.

opened an issue to discuss possible mitigations: https://github.com/flashbots/mev-boost/issues/518
thanks for the report 🙏


wordlist options Suggestion

WORDLIST OPTIONS:
-ep exclude prefix in word list
-es  exclude suffix in word list

-b64 base64 encode for each line of word list
-hex  hex  encode for each line of word list

Hi, do you have an example use cases for -ep and -es suggestions there?
For the later ones, -b64 and -hex, those are already on the roadmap and coming in the near future through the integration of https://github.com/ffuf/pencode

Hello, joohoi:
My dictionary is very large, including jsp, asp, php, some websites have confirmed to use php, do not need to test jsp, asp suffix. Sometimes I use the dirsearch dictionary, the dirsearch dictionary has an extension with the %EXT% flag, maybe add a -replace flag,
-replace "%EXT%:php" to replace extensions in dirsearch, or "-replace ".php:.jsp"" to change some extensions

Hi, do you have an example use cases for -ep and -es suggestions there?
For the later ones, -b64 and -hex, those are already on the roadmap and coming in the near future through the integration of https://github.com/ffuf/pencode

I think create a WORDLIST OPTIONS to perform some basic processing in the wordlist is also conducive to later function expansion

Closing this issue now as I think it has been resolved. Feel free to comment if this is not the case and I'll reopen it.


How to save created image to SVG file ?

Is there a way to export a created image drawn using react-native-svg to a SVG file?
My goal is to:

Use react-native-fs to write the SVG data to a SVG file.
Reference the SVG image from HTML.

Convert HTML to PDF report using react-native-html-to-pdf.

I ran across a brief reference to a new method toDataURL.
While I think writing to a SVG file would be more versatile for PDF report generation,
what format does toDataURL output to? SVG? PNG?
svgRef.toDataURL(base64 => {
    console.log(base64);
});

Thank you for a great component!

You can convert the base64 to PNG format by using other libraries.
It's not what this library do, I'm sorry I can't help you with that.
And for SVG, you can traverse through all children inside Svg component, and serialize them into a string, this is not a bad idea. I'll add this to my TODO list.

@magicismight Thank you for your tip

And for SVG, you can traverse through all children inside Svg component, and serialize them into a string, this is not a bad idea. I'll add this to my TODO list.

Not sure I understand.  I could call toDataURL() on the SVG serialized result?
What would a traverse SVG children and serialize snippet look like?

Can any react native experts share suggestions on how to invisibly render a SVG for image generation purpose?
The VictorChart I use supports a chart.containerRef.svgRef.toDataURL to write image to a file using RNFS, then I add image reference to HTML content, and rendering HTML to PDF..  All good so far.
The hard part, is understanding how to create "dummy view" or a technique to invisibly render an SVG image offscreen ( with width set to match desired PDF page width ) ?

Example: Save SVG as file image using RNFS
  onWriteImageToFile(fileAttachment) {
    RNFS.writeFile(fileAttachment.path, fileAttachment.fileData, fileAttachment.encoding)
      .then(() => {
        console.log('RNFS.writeFile ', fileAttachment.path);
        // this.onWriteFileComplete(fileAttachment, sendCallback);
      })
      .catch((err) => {
        console.log(err);
        Alert.alert(`Failed to write file: ${fileAttachment.name}`, err.message);
      });
  },

In render method, call svgRef.toDataURL()
      <VictoryChart

        ref={(chart) => {
          if (chart && chart.containerRef && chart.containerRef.svgRef) {
            setTimeout(() => {
              chart.containerRef.svgRef.toDataURL((base64) => {
                const name = 'profile.png';
                const path = `${RNFS.DocumentDirectoryPath}/${name}`;
                const fileAttachment = {
                  path,
                  name,
                  fileData: base64,
                  mimeType: 'image/png',
                  encoding: 'base64',
                };
                this.onWriteImageToFile(fileAttachment);
              });
            }, 500);
          }
        }}
      >
      ... < removed code >

Thank in advance,

Exposing the toDataURL API, without having to place the element in the on-screen component tree, would be fantastic.

This appears broken after upgrading react-native and victory-native from 0.7.0 to 0.15.0
"react": "16.0.0-alpha.12",
"react-native": "0.46.4",
"react-native-svg": "^6.0.0-rc1",
"victory-native": "^0.15.0",
It renders however chart.containerRef is undefined?
Is there another way to get chart.containerRef.svgRef ?
    const victoryChart = (
      <VictoryChart
        width={1056}

        ref={(chart) => {
          if (chart && chart.containerRef && chart.containerRef.svgRef) {
            setTimeout(() => {
              console.log('dbg: onSvgCreated timeout');
              chart.containerRef.svgRef.toDataURL((base64) => {
                const name = 'profile.png';

                // Cache Problem: New profile.png images is created but PDF still contains old, even if delete png and pdf first?
                // ToDo: Create a "tmp-<uuid>" folder each time?
                // const path = `${Utility.getFilePath()}/tmp-pdf/${name}`;
                const path = `${Utility.getFilePath()}/${name}`;

                const fileAttachment = {
                  path,
                  name,
                  fileData: base64,
                  mimeType: 'image/png',
                  encoding: 'base64',
                };
                // console.log(base64);
                console.log('fileAttachment:', fileAttachment);
                console.log('this:', this);
                this.onSvgCreated(fileAttachment);
              });
            }, 5 * 1000);
          }
        }}

I hope I do not have to roll back to victory-native 0.7.0 :-(

@boygirl explained on Gitter

victory no longer uses refs for anything other than its portal.
There isn't a built in ref, but you could create a light custom container to save a ref if you need -
you have access to the containerComponent that VictoryChart will render. You can alter that or provide your own.

I do not understand this.  I will do some research on containerComponent

I cannot figure this out how to get a svgRef from containerComponent
Rolling-back to victory-native 0.7.0
I expected this to be easy.
I expect creating a PDF containing a VictoryChart image is a common use case of any charting library.

@esutton adding an optional containerRef prop for VictoryContainer based on this feedback. Tracking here: https://github.com/FormidableLabs/victory/issues/781

For capturing a VictoryChart chart react-native-view-shot worked for me:
https://github.com/gre/react-native-view-shot
  render() {
    const chart = this.renderChart();
    const renderChartOffScreen = this.state.creatingPdf ? chart : null;
    return (
      <View style={{ position: 'absolute', left: 0 }}>
        <ViewShot
          onCapture={this.onCaptureViewShot}
          captureMode="mount"
          options={{
            format: 'png',
            quality: 1.0,
            result: 'base64',
            }}
        >
          {renderChartOffScreen}
        </ViewShot>
      </View>
    );
  }

I was facing an issue in converting my svg component to base 64 using toDataURL after upgrading my rn to 0.55.4 and react-native-svg to 5.5.1. Prior to the upgrade everything was working good. Now I get an error "Unknown bitmap configuration".

Closing as the original issues seems fixed.

An alternative solution if you want vector/svg rather than raster/png:
import * as React from "react";
import { Platform, StyleSheet, TouchableOpacity } from "react-native";
import { Svg, Rect } from "react-native-svg";
import ReactDOMServer from "react-dom/server";

const isWeb = Platform.OS === "web";

const childToWeb = child => {
  const { type, props } = child;
  const name = type && (type.displayName || type.name);
  const Tag = name && name[0].toLowerCase() + name.slice(1);
  return <Tag {...props}>{toWeb(props.children)}</Tag>;
};

const toWeb = children => React.Children.map(children, childToWeb);

export default class App extends React.Component {
  renderSvg() {
    return (
      <Svg height="100%" width="100%" style={{ backgroundColor: "#33AAFF" }}>
        <Rect
          x="50"
          y="50"
          width="50"
          height="50"
          fill="#3399ff"
          strokeWidth="3"
          stroke="rgb(0,0,0)"
        />
      </Svg>
    );
  }
  serialize = () => {
    const element = this.renderSvg();
    const webJsx = isWeb ? element : toWeb(element);
    const svgString = ReactDOMServer.renderToStaticMarkup(webJsx);
    console.log(svgString);
  };
  render() {
    return (
      <TouchableOpacity style={styles.container} onPress={this.serialize}>
        {this.renderSvg()}
      </TouchableOpacity>
    );
  }
}

const styles = StyleSheet.create({
  container: {
    flex: 1,
    justifyContent: "center",
    backgroundColor: "#ecf0f1",
    padding: 8
  }
});


Custom Validation  not working with an optional parameter

Hi, My custom validation is not executing if my parameter is optional. e.g
class AlphaNumeric < Grape::Validations::Base
    def validate_param!(attr_name, params)
        unless params[attr_name] =~ /\A[[:alnum:]]+\z/
            fail Grape::Exceptions::Validation, params: [@scope.full_name(attr_name)], message: 'must consist of alpha-numeric characters'
        end
    end
end

This does not work if i write this
params do
    optional :text, alpha_numeric: true
end

Any suggestion?

Are you passing a value for :text? I believe this is by design not to call the validator if you don't supply an optional parameter. Otherwise make the parameter required.
Reopen if I am missing something.

How about making the value required depending on the value of other parameter?


Cant bootstrap Drupal using Drush from host

I have installed the Drupal-VM  - with a clone of the repo at: https://github.com/geerlingguy/drupal-vm
I am using the box you provide in the example and did a clean vagrant install.  I first deleted from my host computer /etc/exports;  ~/.vagrant.d and the .vagrant file in the root directory of the vm.  I then built the vagrant box and updated it using ssh.
I cannot get into MySQL via SSH.  I get:  ERROR 1045 (28000): Access denied for user 'vagrant'@'localhost' (using password: NO)
I cannot bootstrap Drupal using Drush when I call Drush on my host machine.   I am able to install Drupal on the default address as well as the sub-domains.  I am able to turn modules on and off in the Drupal UI, but cannot use Drush from the host.  I can use Drush from inside the VM.
I also find that manually checking for updates inside Drupal takes a very long time compared to the actual server or other vagrant hosts I have used.
I don't know if these issues are related.  I have used Drush on my host before, not inside the client.  I have not added a a Drush configuration or alias file to the client.

Thanks for the details! I'll hopefully get some more time to respond later (if nobody else does first); but first, please make sure you're glanced through the following Wiki page: Use Drush with Drupal VM.

Thank you Jeff.  I will add the part about the remote host and user and
ssh-config to my aliases.drushrc.php file and try again.  Might have to
wait till tomorrow morning.
Thanks much.
On Mon, Mar 30, 2015 at 3:12 PM, Jeff Geerling<EMAIL_ADDRESS>wrote:

Thanks for the details! I'll hopefully get some more time to respond later
(if nobody else does first); but first, please make sure you're glanced
through the following Wiki page: Use Drush with Drupal VM
https://github.com/geerlingguy/drupal-vm/wiki/Use-Drush-with-Drupal-VM.
--
Reply to this email directly or view it on GitHub
https://github.com/geerlingguy/drupal-vm/issues/60#issuecomment-87795525
.

--
Neil Zusman
(607) 262-6583

I'm going to close this issue and redirect attention to #55; after that issue is complete, Drush usage from the host to the VM should be much simpler!


FEATURE: Add user management module

This adds a user management module to the Neos backend and allows
to delete expired users using a CLI command.

This is a draft for now, as it includes the cleanup up for review as #22 - if that is merged, this one will be easier to review.

@markusguenther this pr is for the 3.x branch wich does not work for 5.x. I tested on 5.1 by locally merging the pr into the package master (which runs on 5.0). If merged this will probably be released as version 3.1 for Neos 4.x and as 4.1 for Neos 5.x

@markusguenther this pr is for the 3.x branch wich does not work for 5.x. I tested on 5.1 by locally merging the pr into the package master (which runs on 5.0). If merged this will probably be released as version 3.1 for Neos 4.x and as 4.1 for Neos 5.x

@markusguenther this pr is for the 3.x branch wich does not work for 5.x. I tested on 5.1 by locally merging the pr into the package master (which runs on 5.0). If merged this will probably be released as version 3.1 for Neos 4.x and as 4.1 for Neos 5.x

Ah ok then this was my fault.
I already fixed the translation thing with the new UI. As the "old" part is using fluid renderings they don't need the content of the xliff.json.


KINE shouldn't log at debug, it's too verbose

If you turn on debug in v0.9.0 you get way too much logs from kine.

Validated using v1.18.10+k3s2 and v1.17.1

Previous state had a ton of logs every few milliseconds when viewing journalctl -u k3s -f after running k3s in debug mode. Small snippet:

Oct 29 18:23:49 ip-172-31-13-174 k3s[4255]: time="2020-10-29T18:23:49.589083222Z" level=debug msg="UPDATE /registry/leases/kube-node-lease/ip-172-31-13-174, value=556, rev=24308, lease=0 => rev=24360, kvrev=24360, updated=true, err=<nil>"
Oct 29 18:23:49 ip-172-31-13-174 k3s[4255]: time="2020-10-29T18:23:49.591039241Z" level=debug msg="TRIGGERED /registry/leases/kube-node-lease/ip-172-31-13-174, revision=24360, delete=false"
Oct 29 18:23:49 ip-172-31-13-174 k3s[4255]: time="2020-10-29T18:23:49.591374263Z" level=debug msg="WATCH READ id=18, key=/registry/leases/kube-node-lease/ip-172-31-13-174, revision=24360"
Oct 29 18:23:49 ip-172-31-13-174 k3s[4255]: time="2020-10-29T18:23:49.591503846Z" level=debug msg="WATCH READ id=19, key=/registry/leases/kube-node-lease/ip-172-31-13-174, revision=24360"
Oct 29 18:23:49 ip-172-31-13-174 k3s[4255]: time="2020-10-29T18:23:49.696539042Z" level=debug msg="LIST /registry/jobs/, start=/registry/jobs/, limit=501, rev=0 => rev=24360, kvs=1, err=<nil>"
Oct 29 18:23:49 ip-172-31-13-174 k3s[4255]: time="2020-10-29T18:23:49.709766306Z" level=debug msg="LIST /registry/cronjobs/, start=/registry/cronjobs/, limit=501, rev=24360 => rev=24360, kvs=0, err=<nil>"
Oct 29 18:23:49 ip-172-31-13-174 k3s[4255]: time="2020-10-29T18:23:49.709797764Z" level=debug msg="LIST /registry/cronjobs/, start=/registry/cronjobs/, limit=501, rev=0 => rev=24360, kvs=0, err=<nil>"

Now, these do not show up at all. Viewing the trace logs should be done when running kine standalone.
Functionality remains the same when running k3s in non-debug mode.


Capture FPS statistics

Measure how frequently we're rendering.  Do to the way CoreAnimation
works we'll only be able to capture FPS up to VSync since we'll never
get called faster than that to draw (the draw itself doesn't actually
capture render time since that's deferred).
Also remove the blocking lines print spew in release builds & switch to
DispatchTime instead of mach_absolute_time for timing that part of the code.

Needs some merging, but otherwise looks good. Thanks!


fix(types): allow custom props define in functional components

Fixes #8855

Thanks for the PR, closed in favor of #10357


Enhance default provider documentation

Bring Wiki and man page documentation in line regarding default provider fall-back behaviour.
Also added return value description of OSSL_PROVIDER_do_all.
Fixes #13844

[x] documentation is added or updated

Needs a second review!

Needs a second review!

This pull request is ready to merge

This pull request is ready to merge

Pushed! Thanks for the contribution.

Pushed! Thanks for the contribution.


Animation error

Describe your problem here.
So i've been using psych engine and wanted to add my custom chracter, so, like any normal being, i pasted my xml file and my spritesheet into: My-Mod - images - characters.
for the .json file i pasted a dad.json file in characters and renamed it with my custom character.
then i go to character editor in psych engine and add the name of the character but it give an error.
Please help me fixing this.
Are you modding a build from source or with Lua?
Lua
What is your build target?
Windows x64
Did you edit anything in this build? If so, mention or summarize your changes.
no

try to use psych engine 0.6.3

try to use psych engine 0.6.3

i tried it and it and it actually kinda worked on the character editor, now i still didn't tested it in game but i'll update you on it, thanks!


GAE: Workaround for unsupported ssl options

Guzzle sets some default ssl context options that are not supported by the App Engine and are creating an error in the log every time an api gets called.
The app will still work but it makes filtering the logs for "real" errors pretty hard.
As said in the App Engine docs the App Engine proxy takes care about certificate verification so we can safely set all the ssl context option to null.
The errors i get without this workaround:
Unsupported SSL context options are set. The following options are present, but have been ignored: cafile
Unsupported SSL context options are set. The following options are present, but have been ignored: cafile
Unsupported SSL context options are set. The following options are present, but have been ignored: cafile

Ok, i really messed up here, sry. So the original line only served guzzle 5 while i was using guzzle 6.
So these changes did actually nothing in guzzle 6 even it worked in my tests.
Also Guzzle 5 throws an exception when this gets used in production (even the tests are working, because i relied on them to test this in guzzle 5 before i installed guzzle 5 in my production app).
So i am gonna close this now and search for a way to remove these ssl context options in guzzle 6 even it doesn't seem like this is possible without some big changes.


Add verdict administrator review. Fixes #6062.

Add new admin.verdicts.review endpoint
Change layout of verdict list and detail view and add forms
Change sort order of the MalwareChecks, and update the tests

Here is a way you can generate verdicts to test this. First open the warehouse shell  docker-compose run web python -m warehouse shell. Then run this:
from warehouse.malware.models import MalwareCheck, MalwareVerdict
from warehouse.packaging.models import File
check = db.query(MalwareCheck).filter(MalwareCheck.name == "SetupPatternCheck").one()
release_files = db.query(File).limit(10)
for f in release_files:
	db.add(MalwareVerdict(release_file=f, check=check, classification="threat", confidence="medium", message="Just a test"))

db.flush()
db.commit()

Really just the thoughts around permissions on the ability to review verdicts, and semantics on the name for the db column. Otherwise, this looks great.


Merge remote-tracking branch 'master' into WebAssembly

Conflicts:
lib/Runtime/Language/Chakra.Runtime.Language.vcxproj.filters

Thanks, JC!


Solve API hard coded in weekly file

Refectory const apiUrl
Alter a API string to apiUrl variable

#8

Hey @acidiney, can you update this PR (merge with master before, because there's a lot going on and your branch is outdated)?
I tried to merge and fix the conflicts locally but it wasn't possible.
There some syntax errors and I wasn't able to figure out why.

I will  create other PR with the soluctions


What are the error models that you have rectified using this project? Have you implemented any technique like machine learning/deep learning in this project? If yes, could you explain it in short to us?

Have used ML(for error detections) and Deep learning (for detections and corrections) in my papers here (Refer publications 2-6): https://www.cse.iitb.ac.in/~rohitsaluja/publication.html  The output of OCR engines can be parsed through these models and you can easily load them as different auxiliary sources. This saves time, if you code for using them on the fly, that needs gpu and on cpu it will slow down the process, so better to create sources in starting of document correction and load them page by page.


C#/F# example doesn't work on clean installation with no topic created

For some reason example stops at message "Consumer initial flow sent 1000" and doesn't go forward. Investigation needed. Normally a new topic should be created automatically and everything working correctly

Cite from https://pulsar.apache.org/docs/en/cookbooks-retention-expiry/

Pulsar brokers are responsible for handling messages that pass through Pulsar, including persistent storage of messages. By default, brokers:
immediately delete all messages that have been acknowledged on every subscription, and
persistently store all unacknowledged messages in a backlog.

In Pulsar, you can override both of these default behaviors, at the namespace level, in two ways:
You can persistently store messages that have already been consumed and acknowledged for a minimum time by setting retention policies.
Messages that are not acknowledged within a specified timeframe, can be automatically marked as consumed, by specifying the time to live (TTL).

So to be able to fist produce messages and then consume them two conditions should be fullfilled

Retention should be set on one level
Consumer should be listening from "earliest" point


As a website owner, I want to measure deployment times, so that I can identify areas for improvement and optimize the deployment process

Acceptance Criteria

The website must record the time when a deployment process begins and
when it is completed.
The website must calculate the duration of the deployment process in minutes
or seconds.
The website must be able to generate reports that show the average
deployment time for a given time period.
The website must be able to filter deployment times by deployment type (e.g.
staging, production).
The website must have a clear and user-friendly interface that allows the user
to view deployment times and reports.
The website must be able to handle errors and exceptions gracefully and
provide informative error messages to the user.
The website must validate user input and provide clear and informative
messages to the user in case of incorrect or missing data.
The user enters incorrect data, such as an invalid deployment type or an
incorrect start or end time.
The website displays informative error messages to the user in case of
incorrect or missing data.
The website handles errors and exceptions gracefully, and does not crash or
display confusing error messages to the user.

Story Points: 2
Estimation: 1-2 days
Resources: deployment tools, testing resources, analytics tools.
Importance: Low
Urgency: High


Fixes for openSUSE / Fedora & general cleanup

Fedora and openSUSE were very broken. I've fixed those and I did a bit of cleanup on the testing. There's more to go, but this is a start towards getting everything passing and getting it all in travis.

Hey thanks for this. I will look it over in the next few days.

looks like this broke the chefspec tests?
$ bundle exec rspec spec/
bundler: failed to load command: rspec (/home/travis/build/svanzoest-cookbooks/apache2/vendor/bundle/ruby/2.1.0/bin/rspec)
Errno::ENOENT: No such file or directory @ rb_sysopen - /home/travis/build/svanzoest-cookbooks/apache2/spec/support/../../test/fixtures/platforms/debian/7.9.json


RedisBungee: Switch to LimeWork RedisBungee fork

Hey, I just saw the introduction for Redis Bungeesupport.
Would it be possible to use Limework's fork instead of the unmaintained RedisBungee library?

Merged in my changes for RedisBungee to Limework's fork of RedisBungee.
Also updated this repo's dependency to Limework's RedisBungee.


FIXFix complex nested and add regress test

Proposed changes
link: https://github.com/apache/doris/pull/26973
Issue Number: close #xxx

Further comments
If this is a relatively large or complex change, kick off the discussion at<EMAIL_ADDRESS>by explaining why you chose the solution you did and what alternatives you considered, etc...

run buildall


more V1.4documentations

Pull Request Test Coverage Report for Build 367

0 of 0   changed or added relevant lines in 0 files are covered.
No unchanged relevant lines lost coverage.
Overall coverage remained the same at 68.02%

Totals

Change from base Build 366:
0.0%

Covered Lines:
2212

Relevant Lines:
3252

💛  - Coveralls


Addopt setuptools-scm without vendoring setuptools-scm

Summary of changes
This is an attempt on implementing the approach described in https://github.com/pypa/setuptools/issues/4530#issuecomment-2261153323.
The main idea is to use setuptools-scm outside of the PyPA build flow and cache the output into files.
Closes
Pull Request Checklist

[ ] Changes have tests
[ ] News fragment added in newsfragments/.
(See documentation for details)

@jaraco this is a draft on the idea in https://github.com/pypa/setuptools/issues/4530#issuecomment-2261153323.
If you agree with the basic idea, I can work to further improve it.


Use v8::ArrayBuffer instead of node::Buffer

Suggest the substitution based on the following facts:

v8::ArrayBuffer can own a buffer (delete the underlying raw buffer in dtor), or borrow a buffer with creation flag ArrayBufferCreationMode::kExternalized) (does not delete), so it's possible to reduce buffer deep copying when using v8::ArrayBuffer in user program
It's easy to allocate views for v8::ArrayBuffer in C++ code, ref issue #89

Ref:

https://github.com/nodejs/node/blob/master/src/node_buffer.h
https://v8docs.nodesource.com/node-6.9/d5/d6e/classv8_1_1_array_buffer.html

@halton @DonnaWuDongxia @tingshao @lisa0314 What do you think of the above issue?

I do not have knowledge to discuss. :) you guys can decide.

Nan:NewBuffer will take the owner ship of the input buffer. I have met with crashes due to double freed memory when ignored that behavior after adding destructor code for some classes. If the ownership can be configured in a explicit way, I think that may be better than the error-prune NewBuffer.


Same prediction for all inputs with inception model

Hi everyone!
I trained an inception model with my custom images (dataset of 8.000 images), then saved it.
And when I try to make predictions with it, whatever is the input images to predict, it's the same output!
I checked everything, and all was seeming good to me...
# Here is the code to train the model
`import os
import numpy as np
import pandas as pd
from scipy.misc import imread
from scipy.misc import imresize
from inception_v3 import InceptionV3
from keras.preprocessing import image
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras import backend as K
import keras
from keras.callbacks import ModelCheckpoint
root_dir = os.path.abspath('.')
data_dir = os.path.join(root_dir, 'data')
os.path.exists(root_dir)
os.path.exists(data_dir)
nb_epoch = int(raw_input("Nb epoch ? : "))
print ('..Debut parsing et resize..')
train = pd.read_csv(os.path.join(data_dir, 'images', 'test.csv'))
train.head()
temp = []
for img_name in train.filename:
image_path = os.path.join(data_dir, 'images', img_name)
img_tmp = imread(image_path, mode='RGB')
img = imresize(img_tmp, (299, 299))
img = img.astype('float32')
temp.append(img)
print ('..Fin parsing et resize..')
train_x = np.stack(temp)
train_x = train_x#, train_x[split_size:]
train_y = keras.utils.np_utils.to_categorical(train.label.values)
base_model = InceptionV3(weights=None, include_top=False)
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(196, activation='softmax')(x)
model = Model(input=base_model.input, output=predictions)
print ('..Debut compilation du model..')
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
print ('..Fin compilation du model..')
filepath="weights-improvement-{epoch:02d}.hdf5"
checkpoint = ModelCheckpoint(filepath, verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint]
print ('Debut entrainement')
trained_model = model.fit(train_x, train_y, nb_epoch=nb_epoch, callbacks=callbacks_list, batch_size=8)
model.save('car_model.h5')
print ('Fin de l'entrainement')
`
## And here is the code to make predictions
`import os
import numpy as np
import pandas as pd
from scipy.misc import imread
from scipy.misc import imresize
from inception_v3 import InceptionV3
from inception_v3 import preprocess_input
from keras.preprocessing import image
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras import backend as K
import keras
from keras.callbacks import ModelCheckpoint
from imagenet_utils import decode_predictions
root_dir = os.path.abspath('.')
data_dir = os.path.join(root_dir, 'data')
os.path.exists(root_dir)
os.path.exists(data_dir)
base_model = InceptionV3(weights=None, include_top=False)
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(197, activation='softmax')(x)
model = Model(input=base_model.input, output=predictions)
print ('..Debut compilation du model..')
model.load_weights('car_model.h5')
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
print ('..Fin compilation du model..')
#while True:
#img_path = raw_input('Img path : ')
img_path = '/home/images/truck1.jpg' # Pas propre. A changer
print (img_path)
img_tmp = imread(img_path, mode='RGB')
img = imresize(img_tmp, (299, 299))
img = img.astype('float32')
x_ = np.expand_dims(img, axis=0)
preds = model.predict(x_)
decode_predictions(preds)
print ('Fin prediction')
`
I hope you'll find the problem, because I don't see at all..
(I trained the model over 50 epochs, so the problem don't come for here)

What are you doing in this method  decode_predictions(preds)?  Can you share the code?

Hi, sure  :
def decode_predictions(preds, top=5):
results = []
for pred in preds:
top_indices = pred.argsort()[-top:][::-1]
for i in top_indices :
print ("prediction is " + i)
return "Prediction Over"
This function is inspired by the function "decode_pred" in the file imagenet_utils.py
But it always return the same top-5 labels no matter what's the input image I try..

I have the same problem. Logits are the same even though inputs are different for inception v4. I also use my custom code for predicting tags for images.

import tensorflow as tf
import inception_base_model
import math
from datetime import datetime
from image_processing import ImageProcessing
import numpy as np
from collections import OrderedDict
from lib.inception_model import inception_base_model as inception

class InceptionOutput(object):
  def __init__(self, checkpoint_dir):
    self.checkpoint_dir = checkpoint_dir

  def output(self, image, num_classes, vocab, threshold=0.5):
    with tf.Session() as sess:
      img_processing = ImageProcessing()
      image = img_processing.process_image(image)
      logits, _ = inception_base_model.inference(image, num_classes=num_classes,
                                                 for_training=False)
      ckpt = tf.train.get_checkpoint_state(self.checkpoint_dir)
      if ckpt:
        variable_averages = tf.train.ExponentialMovingAverage(
          inception.MOVING_AVERAGE_DECAY)
        variables_to_restore = variable_averages.variables_to_restore()
        restorer = tf.train.Saver(variables_to_restore)
        checkpoint_path = ckpt.model_checkpoint_path
        restorer.restore(sess, checkpoint_path)
        print('%s: Pre-trained model restored from %s' %
              (str(datetime.now()), str(checkpoint_path)))

        # Assuming model_checkpoint_path looks something like:
        #   /my-favorite-path/imagenet_train/model.ckpt-0,
        # extract global_step from it.
        global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
        print('Succesfully loaded model from %s at step=%s.' %
              (ckpt.model_checkpoint_path, global_step))
      else:
        print('No checkpoint file found')
        sess.run(tf.initialize_all_variables())
      logits = sess.run(logits)
      return self.get_top_dict(logits[0], vocab, threshold)

  def get_top_dict(self, output_logits, rev_target_vocab, threshold):
    trans_confidence_dict = OrderedDict()
    temp = np.argsort(output_logits)
    top_logit_indices = temp[-5:]
    for logit_index in top_logit_indices:
      # Numpy array has one element. Inside that element is a list of logits for vocab
      trans_logit = output_logits[logit_index]
      #Continue with if the logit index does not exist
      if len(rev_target_vocab) <= logit_index:
        continue
      trans = tf.compat.as_str(rev_target_vocab[logit_index])
      # Faster than tensorflow's sigmoid.
      confidence = 1.0 / (1.0 + math.exp(-trans_logit))
      if (trans not in trans_confidence_dict and confidence >= threshold) or \
        (trans in trans_confidence_dict and confidence > trans_confidence_dict[trans]):
        # Add confidence and translation to dictionary if the key has higher confidence or
        # if the key doesn't exist in dictionary.
        trans_confidence_dict[trans] = confidence
    return trans_confidence_dict

I'm also having the same problem as you! No matter which network I use, I always get the same predictions for different images. This only happens when I try to train my own network and save either its whole configuration or weights.
If I'm using the pre-built weights from VGG, ResNet, whatever, it seems to be working fine.

I have exactly the same problem for keras 2.0.3 and InceptionV3 (custom trained) model.
Pre-build weights from VGG, InceptionV3 works just fine.
But if I train my own top layers - model gives the same prediction for any object.
After training the val_acc is ~95-97%

Did you forget to preprocess your input for the prediction ?

Preprocessing solved my problem

helllllllllo, @leminhlong-pixta i have the same problem as you described above.
can you tell me a bit more specific about "preprocessing" ?
did you solve the problem by subtracting mean or something ?
thanks!

@leminhlong-pixta , Hi , i've trained inception-v4 for my own dataset but i don;t know for prediction for test images , have you some code about prediction ? i use tf-slim.

Having the same problem: output class is always the same for all images I input, even if I do predictions one by one.
I'm using ImageDataGenerator with the corresponding preprocess_input function, then flow_from_directory to feed the images.
Observed in:
Tensorflow's Keras 2.1.4-tf with Python3.6 on Ubuntu 16.04.

Keras pretrained VGG16, using vgg16 preprocess_input inside my ImageDataGenerator. Loading with model = VGG16(weights="imagenet")
Keras pretrained InceptionV3, using inception_v3 preprocess_input, loading with model = InceptionV3(weights="imagenet")

I load my data with:
    datagen = ImageDataGenerator(preprocessing_function=preprocess_input)

    datagen.flow_from_directory(
            data_root_dir, target_size=(image_size[0:2]),
            batch_size=1, save_to_dir='preview', save_prefix='t',
            save_format='jpeg')

And then do the predictions with:
labels_out = model.predict(image)

I see the same behaviour with model.predict_on_batch(image)
Any ideas...?

I have the same problem. Anyone solved the issue?

I have the same issue.  I ran retrain.py against the flowers example.  I ran label_image.py and it worked as expected.  Then I tried to load the model using the LabelImage.java example (passing in Placeholder as input and final_results as output) and I always get the same prediction.

Anybody solves the problem? Thank you!

After img = img.astype('float32'), try to add img = img/255. to make sure every value is between 0 and 1.

After img = img.astype('float32'), try to add img = img/255. to make sure every value is between 0 and 1.

this was myproblem thanks brah

I got the same issue the increase in the training dataset and epochs makes the output probabilities similar for different images. At <10 epochs and dataset of size 10 the model is predicting different probabilities for different images but for more than 100 epochs and dataset of 1000s it generates an exactly same probability distribution for each class.

I am having the same issue. Has anyone solved it? I have tried pre-processing, still having the same trouble.


Use stylelint-config-standard

We're surprisingly quite standard except for three rules.

Removed tab-width from this PR.


Remove configuration form pom.xml

Replaced by this one https://github.com/okteto/microservices-demo/pull/11


RemovedInDjango19Warning: django.utils.importlib will be removed in Django 1.9.

Hi I'm getting this warning which might be far from happen now but may be in some days this must be fixed.
...
/lib/python2.7/site-packages/rpc4django/views.py:21: RemovedInDjango19Warning: django.utils.importlib will be removed in Django 1.9.
  from django.utils.importlib import import_module

I'm working in a pull request for this issue

I'm glad you're working on a PR. I'm not a user of this library I wrote anymore and so I don't maintain it extremely actively.

Oh I found this the only library who worked to me on a project where I have to simulate an old xml-rpc api if you're needing maintainers I could give you a hand.

Any help you can give would be amazing. I merged #42.


-o command line option doesn't work

It's supposed to output .png of canvas.
I've gone back in git history and found it was working in v92 but no longer in v100.
Only a blank canvas is produced in current master branch.

Fixed in https://github.com/sugarlabs/turtleart-activity/pull/55


sparse-training gets contrary  BN distribution

Hi @liuzhuang13,
 After sparse training, I visualize histogram distributions of  all BN scaling factors in our yolov3 model which is used to detect  football,   I found a weird phenomenon--I  didn't get sparser BN scaling factors(more factors equals to 0 or near to 0) but far away from 0  compared with base  model, here is the visualization figs:

implementation of sparse regularization is continuous with your code ,  I am confused about it and expect your  recommends sincerely , and pls tell me  if there is any misunderstanding , thanks.
regards.
summer,Gao

Can you try diagnosis with setting the sparsity regularization very large? In that case, all factors should be close to 0, otherwise there might be some issues in the way it's implemented.

Hi @liuzhuang13,
Thanks for your prompt reply!
As your recommend, I increased coefficient of  sparse regularization(trade-off lambda) from 0.0001 to 0.01 and 0.1, the sparse training result was not improved, here is the histogram distribution of BN scale factors :

they are much farther  away from 0, which  is contrary to our initial  intention.
As for code, I implemented  and trained both in darknet:
1) implement subgradient of absolute value function (same as torch.sign())
2) update BN gradient before  parameters update
here is the code, add following 2 functions before update BN parameters:
sign_gpu(l.n, l.scales_gpu, 1, l.scale_sign_gpu, 1);
axpy_gpu(l.n, lambda, l.scale_sign_gpu, 1, l.scale_updates_gpu, 1);
the cuda implementation of self-defined function sign_gpu() is as following:
extern "C" void sign_gpu(int N, float * X, int INCX, float * Y, int INCY)
{
sign_kernel<<<cuda_gridsize(N), BLOCK>>>(N, X, INCX, Y, INCY);
check_error(cudaPeekAtLastError());
}
  and :

    __global__ void **sign_kernel**(int N, float *X, int INCX, float *Y, int INCY)
    {
        int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
        if(i < N) {
            if(X[i*INCX] > 0) Y[i*INCY] = 1;
            else{
                if(X[i*INCX] < 0) Y[i*INCY] = -1;
                else Y[i*INCY] = 0;
            }
        }
    }

  I have  checked my implementation  and didn't find issue,  is there anything I misunderstand? Expect for your advice sincerely , thanks.

regards,
summer, Gao

Is there a reason to code the additional sub-gradient in cuda, instead of in PyTorch/TF? Sorry I'm not that familiar with cuda coding.  It should be easier in pytorch, and you can indeed add a L1 loss into the original loss function instead of manually add the sign function into the gradients.

Thanks for your quick reply!

The reason why code the additional sub-gradient in cuda is that we have been training our yolov3 model in darknet  and the first thing comes to my mind is to implement in darknet, we can compare the result  with our baseline from precision and time comsuption aspects;
Implement in pytorch is indeed  easier and I will explore yolov3  pytorch training;

Thanks for your suggestions  and reply！
Best regards,
summer, Gao

Fix the issue.
It's the implementation issue  because the bn.gradient I get  before update parameters  in darknet is negative,  now sparse training result is as we expect  (sparser) after multiplying -1 to sign(bn.weight).


[Bug] Straight cannot install the elpa package xeft

What's wrong
Straight seems unable to install xeft, a package on elpa, even after updating recipe repositories.
Directions to reproduce
I used the following config:
(use-package xeft
  :straight t
  :init
  (setq deft-default-extension "org")
  (setq xeft-database "~/.config/emacs")
  (setq xeft-directory "~/Documents/org")
  (setq xeft-recursive t)
  )

It gives the following error:
error: Could not find package xeft. Updating recipe repositories: (org-elpa melpa gnu-elpa-mirror nongnu-elpa el-get emacsmirror-mirror) with ‘straight-pull-recipe-repositories’ may fix this
Executing straight-pull-recipe-repositories works for updates (it seems - let me know if there is a way to confirm this), but does not allow the package to be found. I get the same error. (use-package can seem to update repos though to have xeft show in elpa.).
(as a side note, trying to install directly from the git source also seems to fail as sourcehut (git.sec.ht) is not recognized as a viable host by straight).
Version information

Emacs version: 29.0.60
Operating system: OSX 13.1

By the way, thanks for all the wonderful work on straight. It's great. Love the fork feature and has allowed me to contribute back to emacs packages with bug fixes easily. ❤

Interestingly, the module to be compiled is a bit of an issue, mostly because the Makefile and xapian-lite.cc module need to be in /build rather than /repo (I symlinked them manually but I guess the next restart of emacs had straight nuke them, I guess?
The author of xeft actually asked if you could let us know how we might need to modify the source (or signal to straight it needs to copy those files as well) so as to make this easier for folks on macs.
Btw, thanks for the quick response (and all your amazing work on straight).

Doh... never mind, figured it out myself (someimes it really does help to read the docs... =] ).

(straight-use-package
 '(xeft :repo "https://git.sr.ht/~casouri/xeft"
        :files  (:defaults "*.h" "Makefile" "xapian-lite.cc")))

seems to do the trick. Apologies!

Hmm. Ok, GNU ELPA Mirror probably isn't working properly. I have been working on fixing some problems that were recently reported, and I thought that it had not been broken so long as a month, but I guess that's possible. Either way, the issue should be resolved when I finish fixing https://github.com/radian-software/gnu-elpa-mirror/issues/12.


Search column name with DB Metadata using Sql Server db

Is your feature request related to a problem? Please describe.
It will be nice to be able to search for a column directly from DB Metadata
Describe the solution you'd like
Add in the Searcb->DB Metadata the Column object type for MS SQL Server

Describe alternatives you've considered
Additional context
the script for doing that is as follows:
select table_name, Column_name, * from information_schema.columns
where column_name = 'COLUMN_NAME'

Hello @mihaimarin83
Thanks for the idea.

This issue seems to be very close to #8684 . This issue is related to Oracle DB instead of SQL Server DB but maybe the solution is similar ?

Bump for this Feature Request. I use DBeaver for work and this would be a great benefit.


Snitches sometimes fail to refresh

Reported by multiple players.
Potential fix staged by #19

The fix is here: https://github.com/CivMC/CivModCore/pull/78
Though #19 is also useful.

Thank you for the clarification

https://github.com/CivMC/CivModCore/pull/78


Remove state concepts from the useProduct hook

The current useProduct hook mixes both actual product data from the API and current state. Separating the state management related concepts solely into the useProductOptions hook makes it clear which should be used for interactive state management on the client, versus used for product data, on both the server and client.
useProduct
From the return object, remove selectedVariant, setSelectedVariant, setSelectedOption, setSelectedOptions, isOptionInStock, setSelectedSellingPlan, selectedSellingPlan, selectedSellingPlanAllocation, sellingPlanGroups
useProductOptions
Stays the same.

Background
useProductOptions sets client-side-only state in hooks, to determine things like "which variant is selected".
The ProductProvider context provider uses useProductOptions hook inside of it, so that things like the selected variant are accessible anywhere and also so that it remains consistent across components.
Components like SelectedVariantPrice.client.tsx access selectedVariant from useProduct() because they want to get that consistent context value.
Solutions
If we want to remove things like selectedVariant from ProductProvider, I think our choices are:

Create a new provider called ProductOptionsProvider that can provide those stateful values for useProductOptions()
Remove context usage, call useProductOptions once at a high level (in the demo app), and then pass stuff like selectedVariant down as props

It's probably easier to create a new ProductOptionsProvider than to pass things down, and will certainly be an easier migration path for devs too.
Hopefully makes it easier to manage things like deep-linking variants through the URL as well? Not sure on that.
Anyway, just wanted to make sure we're aligned.

Let me make sure this makes sense, because it's hurting my brain right now for some reason:
useProducOptions() uses state, so it can't pull from context.
The logic currently used in useProductOptions() will now be used internally in the context provider.
So, I could move all the logic / statefulness out of useProductOptions() and put it directly into ProductOptionsProvider.
Then, I can change useProductOptions() to simply do:
export function useProductOptions() {
  const context = useContext(ProductOptionsContext);

  return context;
}

Does that seem right?

But that's a breaking change for useProductOptions(). Before, it was keeping its own state and if anyone was using it, they had their own stateful logic local to that component. But now, it would be pulling from a context provider, so that stateful logic could now be shared between components. That fundamentally changes how the hook works.
So should we

Rename useProductOptions() to something like useProductSelectionState() (along with all the docs), and then use  useProductOptions() for the hook that pulls from context? In this case, useProductOptions() is the one mostly used by devs.
Keep useProductOptions() as the one that manages state, and create a new hook useProductOptionsContext (names are bad) as the one that most people should use? In this case, useProductOptionsContext() is the one mostly used by devs.

....my brain hurts.
@elisenyang @jplhomer

OK I see what you're saying. Stepping back, we really have two problems we're trying to solve:

We can't encode state (selectedVariantId) or non-serializable values (setSelectedVariant) in the ProductProvider. This is because we plan to support server context which executes in server components (no state) and has to be serialized (no functions).
It's confusing for the useProduct() hook to return stateful stuff sometimes (in client components) and only data other times (server components).

I think the most critical problem to solve is Problem 1. We're solving it by rendering a client component inside the ProductProvider which stores state in its own client-only context.
I'd like to propose not solving Problem 2:

In a server component, useProduct() returns data
In a client component, useProduct() returns data AND state
We don't modify useProductOptions() at all

We can do this by merging the two context values inside the useProduct() hook:
export function useProduct() {
  const data = useContext(ProductContext);
  const state = useContext(ProductOptionsContext);

  return {
    ...data,
    ...state
  };
}

We could even implement helpers which throw when you try to access state in server components, similar to what React does for "hook not implemented" errors:
function hookNotImplemented(name) {
  return () => throw new Error('...');
}

export function useProduct() {
  const data = useContext(ProductContext);
  const state = useContext(ProductOptionsContext);

  return {
    ...data,
    setSelectedVariant: state.setSelectedVariant ?? hookNotImplemented('setSelectedVariant'),
    // ...
  };
}

I think this preserves the simple developer experience we aimed to create when we first added the useProduct() hook and provider without adding multiple hooks with funky names.
What are your thoughts?

Thanks for writing that out, makes sense. PR is up, hopefully it makes sense.


Implement TTS WS

Implements/Addresses: https://github.com/deepgram/deepgram-dotnet-sdk/issues/306
This PR implements TTS WS. In this PR, I restructured the Speak/TTS clients because we are introducing a WS Client, and the REST client was at the root of the Deepgram.<Clients | Models>.Speak.v1 folder would place the REST and WS client in the same namespace. This is awkward and can be confusing. This follows the Go SDK, which places each into the respective namespaces:

Deepgram.<Clients | Models>.Speak.v1.REST
Deepgram.<Clients | Models>.Speak.v1.Websocket

The REST client has been renamed SpeakClient -> SpeakRESTClient. References to the old (now obsolete) REST client in Deepgram.<Clients | Models>.Speak.v1 references have been marked obsolete but still exist for backward compatibility. The references to the examples for the Speak REST clients are intentionally pointing to the obsolete namespaces to prove backwards compatibility. These will be updated in a subsequent PR.
This implements a simple example to demonstrate the functionality and updates the Speak REST unit tests.

@coderabbitai review


Module import capitalisation isn't enforced

I'm wondering why hapi-capitalize-modules isn't strictly enforced, even though the Hapi style guide isn't ambivalent on this rule.

This is a wider issue than just hapi-capitalize-modules. There don't seem to be any rules for what generates a warning and what generates an error. IMO, we should just use error level throughout. I started this from a config file that was in lab. Maybe @geek can shed some light/feedback on this?

@cjihrig I would like to error on this as well

I can submit a tiny PR for this. Just to be clear, going full throttle on this would mean it should apply everywhere and not just on global-scope-only, correct?

@geek how would you feel about having all enforced rules have a severity of 2 (error). I don't really see a point to having warning level linting rules, since we pretty much treat them as errors.
@prashaantt no, we should still only enforce this on the global scope. See https://github.com/hapijs/lab/pull/368#discussion_r31950100. I thought your question had to do with rule severity.

@cjihrig With global-scope-only I seem to be getting no warnings for any scope at all. I can only get this to actually lint by removing the global scope option. I'll try writing a test to show what I mean.

Seems like these rules should be errors and Lab linting should only fail if there are errors not when there are only warnings? (just a thought)

@prashaantt the problem you're seeing with global-scope-only came from a breaking change in ESLint 1.4.0. My guess would be https://github.com/eslint/eslint/issues/3700 specifically.

Published<EMAIL_ADDRESS>with the fix. Added a regression test in this module in #30.

Published v8.0.1 everything should automatically get picked up by lab.

@cjihrig Thanks, works as expected now.


[WIP][SPARK-37163][SQL] Disallow casting Date as Numeric types

What changes were proposed in this pull request?

Disallow casting Date as Numeric types, which always returned NULL in Spark 3.2 or earlier.
If users really want to fall back to the legacy behavior, they can set the legacy flag spark.sql.legacy.allowCastDateAsNumeric as true.
Why are the changes needed?

Currently, Date type values can be cast as Numeric types. However, the result is always NULL.
On the other hand, Numeric values can't be cast as Date type.
It doesn't make sense to keep the behavior of casting Date to NULL numeric types. I suggest to disallow the conversion.
Does this PR introduce any user-facing change?

Yes. Disallow casting Date as Numeric types. If users really want to fall back to the legacy behavior, they can set the legacy flag spark.sql.legacy.allowCastDateAsNumeric as true.
How was this patch tested?

UT

Had some offline discussion. I decide to hold this one.


catalog: handle the removal of tags gracefully

Now, when a tag gets removed by the catalog job, its activities will be updated
by saving the tag_name extra parameter. This parameter will be used by the
views to handle missing tags. Moreover, I've added more log messages.
Fixes #506
Fixes #443
Signed-off-by: Miquel Sabaté Solà<EMAIL_ADDRESS>

LGTM


🍒[5.9][IRGen] Distributed: Destroy loaded arguments after the call

Description: Decoding of remote call arguments is synthesized in IR, and the lifetime of the arguments that have been loaded due to underlying method conversion have to be destroyed after the call otherwise they are going to leak. This PR fixes this lifetime issue resolving the memory leak of arguments passed to remote call targets.
EDIT: Added the follow up from https://github.com/apple/swift/pull/65952 immediately here; which fixes the order in which we destroy and dealloc: we MUST deallocate AFTER we destroy since otherwise the destroy does not make the refcount drop to zero, and we were still missing deinitializers running. The patch now contains complete fix.
Risk: Low, isolated to distributed remote calls; confirmed to work on all platforms
Review by: @jckarter
Testing: CI testing
Original PR: https://github.com/apple/swift/pull/65874
Radar: rdar://109207043
Resolves: #65853

@swift-ci please test

Weird failure, I'll investigate
<unknown>:0: error: error opening '/home/build-user/build/buildbot_linux/swift-linux-x86_64/test-linux-x86_64/Distributed/Output/distributed_actor_thunk_doesnt_leak_class_arguments.swift.tmp/FakeDistributedActorSystems.swiftmodule' for output: No such file or directory
and I'll destroy them in reverse order as we create

@swift-ci please test

Fixed formatting

@swift-ci please test

@swift-ci please test

@swift-ci please test

What; I'm checking this exact same branch locally and the test passes... Looking into it hmm

Could have been the missing empty directory directive hmmm

@swift-ci please test

Interesting, that was it so I'll bring that 1803771 (#65941) over to main too


HSEARCH-3467 Add tests for all the attributes validated by the Elasticsearch schema validator

https://hibernate.atlassian.net/browse/HSEARCH-3467
Still in preview, since several attributes have not been tested yet: done

Property type attributes: boots removed!, store, null_value, fielddata, doc_values, normalizer done!.
Normalizer definition attributes: char_filter, token_filter. already exists
Analyzer definition attributes: char_filter, token_filter, tokenizer. already exists

Pull Request Test Coverage Report for Build 1

0 of 0   changed or added relevant lines in 0 files are covered.
No unchanged relevant lines lost coverage.
Overall coverage increased (+0.005%) to 88.596%

Totals

Change from base Build 204:
0.005%

Covered Lines:
16696

Relevant Lines:
18845

💛  - Coveralls

@yrodiere thank you for the review.
I ran this build: http://ci.hibernate.org/view/Search/job/hibernate-search/job/PR-1953/5/parameters/
to check the behavior for all supported ES versions.

Thanks @yrodiere. I think I've addressed the points here.
I started an new full built: http://ci.hibernate.org/job/hibernate-search/view/change-requests/job/PR-1953/7.

Merged, thanks!

Thanks @yrodiere. I'm reviewing yours.