Somes issues and how to enable cuda support

When you first run "python app.py" it complains there is no spaces module installed.  Once you fix that it informs you the Cuda provider is not being used.
I added onnxruntime-gpu and spaces to the requirements.txt.
Then installed Nvidia computing toolkit 12.6 and the cudnn 9.5 Installer (win10) on my Windows 11 machine.  For cudnn go to
C:\Program Files\NVIDIA\CUDNN\v9.5 and copy the files in bin, include, and lib into the similar locations in:
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.6
You won't get the Cuda exeuction provider warning any more but the program will still use the cpu - to fix it do the next step.
Finally go to https://pytorch.org/get-started/locally/ - select the appropriate options - I used cuda 12.4 install command within the conda environment.

When you first run "python app.py" it complains there is no spaces module installed. Once you fix that it informs you the Cuda provider is not being used.
I added onnxruntime-gpu and spaces to the requirements.txt.
Then installed Nvidia computing toolkit 12.6 and the cudnn 9.5 Installer (win10) on my Windows 11 machine. For cudnn go to
C:\Program Files\NVIDIA\CUDNN\v9.5 and copy the files in bin, include, and lib into the similar locations in: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.6
You won't get the Cuda exeuction provider warning any more but the program will still use the cpu - to fix it do the next step.
Finally go to https://pytorch.org/get-started/locally/ - select the appropriate options - I used cuda 12.4 install command within the conda environment.

Thanks, I forgot to update these instructions :)


Add support for inline ECP and use context for mocking

Remove unnecessary default values

Support inline policy in "validate image"

Experiment with context to mock external call

Based on the pattern defined in:
https://incident.io/blog/golang-client-mocks

The pattern is only currently used in a couple of places as a mechanism
to illustrate the pattern.

Mocks are not auto generated at this time, but it's something to
consider if this pattern is adopted throughout the code base.

There are other placed in ec-cli that read the ECP from kubernetes that can also be updated. I purposely kept it out of this PR as to keep it to a reasonable size.

One more thing™, do we want to version acceptance/.sigstore, perhaps we should put it in .gitignore...

One more thingtm, do we want to version acceptance/.sigstore, perhaps we should put it in .gitignore...

@zregvart. I have not idea :smile: Maybe we can remove it on a follow up PR?

Nitpick: Use consistent import alias for github.com/hacbs-contract/enterprise-contract-controller/api/v1alpha1. I think it makes sense to replace ecp with ecc as you did in a few locations in this change.

@robnester-rh, added a commit for this. Thanks.


find_package prevent usage of this tools ?

On a project with find_package, this tool provides:
[glow@arch ~/tmp/cmake_min_version]$ python cmake_min_version.py ~/dev/f3d/f3d/src/examples/
Found 25 CMake binaries from directory tools

[  0%] CMake 3.13.5 ✘ error
       CMakeLists.txt:5 (find_package)
[ 17%] CMake 3.19.8 ✘ error
       CMakeLists.txt:5 (find_package)
[ 33%] CMake 3.22.6 ✘ error
       CMakeLists.txt:5 (find_package)
[ 50%] CMake 3.24.3 ✘ error
       CMakeLists.txt:5 (find_package)
[ 80%] CMake 3.25.2 ✘ error
       CMakeLists.txt:5 (find_package)
[100%] ERROR: Could not find working version.

Not sure if this is an issue or a limitation.

It's not designed not to work, so this is a bug. Do you have any example project that exhibits this bug? Which OS are you using?

I've been try to use it an any simple project like this one:
project(check-engine)

cmake_minimum_required(VERSION 3.1)

find_package(f3d REQUIRED)

add_executable(check-engine main.cxx)
target_link_libraries(check-engine f3d::libf3d)

# make sure the libf3d API is compatible with C++11
set_target_properties(check-engine PROPERTIES CXX_STANDARD 11)

it fails on find_package

It might not be the same problem as @mwestphal, but I encountered something similar with FindPkgConfig.cmake when trying to use cmake_min_version. In my case some packages were not installed in default system locations and explicitly setting the CMAKE_PREFIX_PATH environment variable fixed the problem for me:
CMAKE_PREFIX_PATH=~/projects/foobar/prefix venv/bin/python cmake_min_version.py ~/projects/foobar/repo

Even though this worked for my case, it would be nice to be able to control what gets passed to the underlying CMakes explicitly with a script option or similar.

Even though this worked for my case, it would be nice to be able to control what gets passed to the underlying CMakes explicitly with a script option or similar.

Do you mean command-line options or environment variables?


C/C++ dependency analysis can handle all of the boost library headers

Split out of #279, as an additional piece of work. The boost libraries pull some interesting preprocessor tricks that currently baffle the C/C++ dependency analysis, including the improvements implemented as part of #279, so that the analysis gives up and treats all of the include directories as inputs. This kills the developer experience.
The boost libraries are super popular and so we should invest some effort in improving the dependency analysis so that it can understand all of the boost library headers.

I spend a bit more time looking into this. I think it would be good to change approach to a greater or lesser degree, and we should return to this after tackling #302 (aka make what we have faster).
Specifically, it seems all of the complexity in the boost headers is actually hidden behind some simple #ifdef checks for macros that are only used for development of boost itself. So, rather than trying to understand all of the complex #define expressions in boost we might instead understand simple #ifndef expressions and ignore all the complexity. We might do this by continuing to understand the source ourselves, asking the compiler to do the preprocessing or both (with one being the fallback for the other, as we can't use one of the approaches for everything).
Regardless of the implementation, this analysis is going to need to know the macros that will be defined at compile time, which are not present on the Depend task. I'd defer changing this until we know the Depend task's fate, as #302 may see it disappear or reworked in a major way (or not).

@lacasseio Will this issue be dealt with in #302? I'm getting the MD5 error because the boost headers cannot be parsed and all include directories are considered which in turn would include the .gradle directory...
Not sure which ticket is the one to watch. Can you please clarify? Thanks :)

Yes, it's a common issue users have being reporting. Could you confirm this is what you are seeing: Basically, you are using some Boost library and including headers that Gradle fails to parse because it finds something like #include MACRO and your includes are at the root of your project where the .gradle folder get generated.
The workaround would be to ensure your includes aren't in your project directory but at least one folder down (such `includes). It's far from ideal but it's the best solution.
To help direct our work toward a better fix, could you tell us what libraries and possibly #include you have inside the Boost library. Better yet, if you could provide a sample project, with just the C/C++/headers and their respective #include directives. The code itself (functions, etc.) is relevant for us to reproduce your issue.

@lacasseio Thanks for the quick response - and sorry for the delay. :flushed:
Actually as you assumed we're using some boost headers for our application. Currently the folder containing the .gradle directory has to be included as some include headers get referenced by <folder_name/header.h>. Since folder_name is at the same level like .gradle we get the issue.
Moving the headers into different folders would probably be a solution. I tried it for one module and it seems to work.
About the example: Not sure what exactly I should provide. Will an application which uses the folder where .gradle is located as include directory + uses some header files containing macros in at least one of the #include directives be fine? Or does it have to be boost?
Unfortunately I cannot send any code as is so I wanted to create a small example project which reproduces the issue. However while working on that example I realized that I'm not 100% sure about what would help here.
Would a very small example - without boost - help?


Added examples

Added two examples

Awesome! Any places I can add the blog post links?

You can create new category.

Closing due to inactivity.
Please open a new issue with a reference to this one if you can follow up with more information.


[Update] Ported my part of user stories

Pull Request Test Coverage Report for Build 22

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 92.811%

Totals

Change from base Build 19:
0.0%

Covered Lines:
1433

Relevant Lines:
1544

💛  - Coveralls


Deadlines in tests

Summary:

Uses deadlines (durations in any time unit) instead of iterations
gtest extended with ASSERT_NO_ERROR which prints std::error_code messages on error.
system::poll returns std::error_code. ASSERT_NO_ERROR will have more uses in the future as more code migrates to error_code.
Deadline scale factor support

The scale factor is an environment variable which makes it easier to experiment with deadlines (instead of recompiling).
It can also be set when running tests on slow machines.
For instance, to see if 10% of the deadline (1 second in this case) is enough to pass the node_receive_quorum test:
DEADLINE_SCALE_FACTOR=0.1 GTEST_FILTER=node.node_receive_quorum Debug/core_test
To allow 20% more time on all tests on a slow machine, a Travis config or whatever can export:
DEADLINE_SCALE_FACTOR=1.2
Sample error when using ASSERT_NO_ERROR:
node.cpp:230: Failure
Value of: system.poll ()
  Actual: Deadline expired
Expected:

bulk.offline_send seems to have been failing for a while, on master as well

I'm thinking this should merge before it causes more conflicts.


Archive a CSV

(Fill in the relevant information below to help triage your issue.)

Q
A

Version
9.2

Question
How can I archive a created CSV File?

@engharb thanks for using the library.
What do you mean by archiving a created file ? do you have an example ?

@nyamsprod after I create a CSV I want to zip it 'to compress'. Is there a way how to do? I have tried  different ways with no success.
$csv = Writer::createFromPath('php://temp', 'w+');
$csv->insertOne($testData);

I want this $csv object to be zipped.

CSV can be used with resource so I would suggest using the following snippet. Not tested but should do the trick*
<?php
use League\Csv\Writer;

$stream = gzopen("/path/to/compress/file.csv.gz", "w");
$csv = Writer::createFromStream($stream);
$csv->insertOne($record);
unset($csv);
gzclose($stream);


examples: add example queries

These are some queries which we've used in the past. They may be useful for others who want to write their own queries.
Please let me know if I can make any changes which would make this better.
/cc @embidem

Thanks @xoebus


api-umbrella doesn't start Centos 7 : control process exited, code=exited status=132

I am trying to install api umbrella from the binary package for centos 7, the installation was successful, but when I try start api-umbrella /etc/rc.d/init.d/api-umbrella start it return failed
[root@xxx yum.repos.d]# /etc/rc.d/init.d/api-umbrella start
Starting api-umbrella (via systemctl):  Job for api-umbrella.service failed because the control process exited with error code. See "systemctl status api-umbrella.service" and "journalctl -xe" for details.
[FAILED]
And any log was created, the journalctl -xe shows,
Oct 06 11:03:32 mcu2 polkitd[682]: Registered Authentication Agent for unix-process:6794:430755 (system bus name :1.168 [/usr/bin
Oct 06 11:03:32 mcu2 [1]: Starting LSB: API Umbrella...
-- Subject: Unit api-umbrella.service has begun start-up
-- Defined-By: systemd
-- Support: http://lists.freedesktop.org/mailman/listinfo/systemd-devel
-- Unit api-umbrella.service has begun starting up.
Oct 06 11:03:32 mcu2 kernel: traps: nginx[6824] trap invalid opcode ip:7f253390ca5e sp:7ffcb32e1030 error:0 in libluajit-5.1.so.2
Oct 06 11:03:32 mcu2 abrt-hook-ccpp[6825]: Process 6824 (nginx) of user 0 killed by SIGILL - dumping core
Oct 06 11:03:32 mcu2 abrt-hook-ccpp[6825]: Failed to create core_backtrace: dwfl_getthread_frames failed: No DWARF information fo
Oct 06 11:03:32 mcu2 api-umbrella[6800]: Starting api-umbrella: [FAILED]
Oct 06 11:03:32 mcu2 systemd[1]: api-umbrella.service: control process exited, code=exited status=132
Oct 06 11:03:32 mcu2 systemd[1]: Failed to start LSB: API Umbrella.
-- Subject: Unit api-umbrella.service has failed
-- Defined-By: systemd
-- Support: http://lists.freedesktop.org/mailman/listinfo/systemd-devel
-- Unit api-umbrella.service has failed.
-- The result is failed.
Oct 06 11:03:32 mcu2 systemd[1]: Unit api-umbrella.service entered failed state.
Oct 06 11:03:32 mcu2 systemd[1]: api-umbrella.service failed.
My linux distribution is Centos 7, and the version of api umbrella installed is 0.14.4-1
Please,  can anybody help me to solve this problem?

I have been testing in centos 6.9 and ubuntu 16, and I have the same

Hm, the fact that it's core dumping is pretty strange. Are you getting the same "trap invalid opcode" error in CentOS 6.9 and Ubuntu 16? Is this a 64 bit machine you're installing the packages on (we only provide 64bit binaries)? And how much RAM does this system have (we typically recommend 1.5GB minimum).

Hello, yes i had the same "trap invalid opcode" error in CentOS 6.9 and Ubuntu 16, mi machin is of 64bits, core 2 duo with 2 gb of RAM

@GUI , Finally, I use an instance in open stack and everything work without problems, the installation was successful without errors, and the api-umbrella works correctly. I have been reading about this problem and I found that maybe the issue was related with my processor architecture because openrestry needs some instructions that the core 2 duo processor doesn't have.


[Chore] Unittest for dbt tasks

PR checklist

[ ] Ensure you have added or ran the appropriate tests for your PR.
[ ] DCO signed

What type of PR is this?
What this PR does / why we need it:
Which issue(s) this PR fixes:
Special notes for your reviewer:
Does this PR introduce a user-facing change?:

Pending task


feat: Update circuits structs with gas info

Adds gas limits, max fee per gas, actual fee per gas, and gas used to circuit structs. These structs are not populated for now, they are just defined. Also updates the corresponding definitions in the yellow paper.
Follows the spec outlined by @just-mitch in #5565 but for the following changes:

GasLimits is renamed to GasSettings (following @iAmMichaelConnor suggestion)
Info for each dimension in GasSettings (da/l1/l2) is bundled into a DimensionGasSettings struct
GasSettings in PrivateKernelPublicInputs is stored inside the ConstantData struct
Bundled each fee_per_xx_gas field in GlobalVariables into a GasFees struct
Did not yet add the pending_header in CombinedConstantData

#5677  👈
master

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Benchmark results
Metrics with a significant change:

note_history_trial_decrypting_time_in_ms (5): 130 (+198%)
note_trial_decrypting_time_in_ms (32): 23.8 (-79%)

Detailed results
All benchmarks are run on txs on the Benchmarking contract on the repository. Each tx consists of a batch call  to create_note and increment_balance, which guarantees that each tx has a private call, a nested private call, a public call, and a nested public call, as well as an emitted private note, an unencrypted log, and public storage read and write.
This benchmark source data is available in JSON format on S3 here.
L2 block published to L1
Each column represents the number of txs on an L2 block published to L1.

Metric
8 txs
32 txs
64 txs

l1_rollup_calldata_size_in_bytes
772 (+14%)
772 (+14%)
772 (+14%)

l1_rollup_calldata_gas
6,796 (+6%)
6,796 (+6%)
6,784 (+6%)

l1_rollup_execution_gas
587,277
587,277
587,265

l2_block_processing_time_in_ms
1,316 (-1%)
4,654 (-2%)
9,263 (-2%)

note_successful_decrypting_time_in_ms
214 (-12%)
515 (+2%)
923 (+1%)

note_trial_decrypting_time_in_ms
17.7 (-59%)
:warning: 23.8 (-79%)
42.9 (+43%)

l2_block_building_time_in_ms
10,483 (+1%)
37,701 (+1%)
73,590 (-1%)

l2_block_rollup_simulation_time_in_ms
10,369 (+1%)
37,297 (+1%)
72,823 (-1%)

l2_block_public_tx_process_time_in_ms
5,015 (+2%)
19,702 (+2%)
39,348 (+1%)

L2 chain processing
Each column represents the number of blocks on the L2 chain where each block has 16 txs.

Metric
5 blocks
10 blocks

node_history_sync_time_in_ms
14,477 (-1%)
26,484 (-4%)

note_history_successful_decrypting_time_in_ms
1,136 (+2%)
2,143 (-4%)

note_history_trial_decrypting_time_in_ms
:warning: 130 (+198%)
74.9 (-4%)

node_database_size_in_bytes
18,657,360
35,012,688

pxe_database_size_in_bytes
29,859
59,414

Circuits stats
Stats on running time and I/O sizes collected for every circuit run across all benchmarks.

Circuit
circuit_simulation_time_in_ms
circuit_input_size_in_bytes
circuit_output_size_in_bytes

private-kernel-init
145 (+1%)
44,561 (+1%)
26,424 (+1%)

private-kernel-inner
154 (+1%)
72,188 (+1%)
26,424 (+1%)

private-kernel-ordering
109 (-3%)
51,090 (+1%)
39,297 (+1%)

base-parity
3,884
128
311

root-parity
1,041 (-7%)
1,244
311

base-rollup
13,168 (-2%)
110,820
957 (+11%)

root-rollup
49.2 (-1%)
4,551 (+4%)
821 (+13%)

public-kernel-app-logic
84.3 (+6%)
48,151 (+1%)
40,933 (+1%)

public-kernel-tail
122 (+8%)
53,644 (+1%)
7,385 (+4%)

merge-rollup
10.7
2,760 (+7%)
957 (+11%)

public-kernel-teardown
78.5 (+6%)
48,151 (+1%)
40,933 (+1%)

public-kernel-setup
77.5 (+2%)
48,151 (+1%)
40,933 (+1%)

Tree insertion stats
The duration to insert a fixed batch of leaves into each tree type.

Metric
1 leaves
16 leaves
64 leaves
128 leaves
512 leaves
1024 leaves
2048 leaves
4096 leaves
32 leaves

batch_insert_into_append_only_tree_16_depth_ms
9.86 (-1%)
15.9 (-2%)
N/A
N/A
N/A
N/A
N/A
N/A
N/A

batch_insert_into_append_only_tree_16_depth_hash_count
16.8
31.6
N/A
N/A
N/A
N/A
N/A
N/A
N/A

batch_insert_into_append_only_tree_16_depth_hash_ms
0.575 (-1%)
0.492 (-2%)
N/A
N/A
N/A
N/A
N/A
N/A
N/A

batch_insert_into_append_only_tree_32_depth_ms
N/A
N/A
45.6 (-2%)
71.0 (-2%)
230 (-1%)
460 (+3%)
869
1,727
N/A

batch_insert_into_append_only_tree_32_depth_hash_count
N/A
N/A
96.0
159
543
1,055
2,079
4,127
N/A

batch_insert_into_append_only_tree_32_depth_hash_ms
N/A
N/A
0.468 (-1%)
0.439 (-2%)
0.420 (-1%)
0.431 (+4%)
0.413
0.413
N/A

batch_insert_into_indexed_tree_20_depth_ms
N/A
N/A
53.8 (-2%)
105 (-2%)
335 (-1%)
659 (-1%)
1,309
2,609 (-1%)
N/A

batch_insert_into_indexed_tree_20_depth_hash_count
N/A
N/A
105
207
691
1,363
2,707
5,395
N/A

batch_insert_into_indexed_tree_20_depth_hash_ms
N/A
N/A
0.476 (-1%)
0.473 (-2%)
0.457
0.454
0.454
0.453 (-1%)
N/A

batch_insert_into_indexed_tree_40_depth_ms
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
60.5 (-1%)

batch_insert_into_indexed_tree_40_depth_hash_count
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
109

batch_insert_into_indexed_tree_40_depth_hash_ms
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
0.529 (-1%)

Miscellaneous
Transaction sizes based on how many contract classes are registered in the tx.

Metric
0 registered classes
1 registered classes

tx_size_in_bytes
41,005 (+2%)
496,889

Transaction size based on fee payment method

Metric
native fee payment method
fpc_public fee payment method
fpc_private fee payment method

tx_with_fee_size_in_bytes
905
1,161
1,377

Transaction processing duration by data writes.

Metric
0 new note hashes
1 new note hashes
2 new note hashes

tx_pxe_processing_time_ms
1,249 (+9%)
894 (+9%)
4,210 (+10%)

Metric
1 public data writes
2 public data writes
3 public data writes
4 public data writes
5 public data writes
8 public data writes

tx_sequencer_processing_time_ms
443 (+4%)
301 (+9%)
767 (+8%)
383 (+8%)
1,254 (+3%)
454 (+9%)


Automated Package Upgrade

:tada: This PR is included in version 2.2.431 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


upgrade build tools so that we can use ES6+ conventions in source code

@therewasaguy I think we have to solve this before going to convert the src code to es6. what do you think?

@therewasaguy I found a blog for converting the es6 to es5. would you take a look.

@therewasaguy can you tell me how I can reproduce this behavior
Running "requirejs:unmin" (requirejs) task
{ Error: Error: Line 40: Unexpected reserved word
    at throwError (/Users/jasonsigal/src/p5/p5.js-sound/node_modules/esprima/esprima.js:1846:21)

    at /Users/jasonsigal/src/p5/p5.js-sound/node_modules/requirejs/bin/r.js:30099:19

Can you provide me some sample code for this?


psycopg3 compatibility?

I was wondering if pg_activity is compatible with psycopg3. If no, would it be difficult to move to psycopg3 ?
Thanks.

It is compatible with psycopg 3+ already, alongside with psycopg2 as a "legacy" alternative (mostly for distributions): https://github.com/dalibo/pg_activity/blob/master/CHANGELOG.md#pg_activity-310---2023-03-01

OK, good to know. It looks like you need python3-typing-extensions RPM package if you want to use psycopg3 with pg_activity. It wasn't needed with psycopg2, but OK, I'm fine with that. Thank you.

It looks like you need python3-typing-extensions RPM package if you want to use psycopg3 with pg_activity.

If you're using the package from yum.postgresql.org, it's a packaging
bug: https://redmine.postgresql.org/issues/7781

Oh, interesting. Thank you. 5-months-without-answer ticket, probably won't move anytime soon :(


gui: Adding to many ports for a container breaks workflow

When a new container is created and ports are allocated but not used, the deploy of the container no longer works. The only way to back out of this is create the container again as it is not possible to delete the unused empty port field.

Shipyard v3 has been released and is a major refactor. Could you please check to see if this is still an issue? If so, please re-open. Thanks!


Duplicating a chart leads to an application error

Describe the bug
In editor mode, when copying a chart, an application error is shown

To Reproduce
Steps to reproduce the behavior:

Go to https://test.visualize.admin.ch/en/v/g00DSr22kUJ7?dataSource=Prod
Click on Copy and edit thsi visualization
in the editor mode, duplicate the chart (by using the option on the tab)
See error

Expected behavior
the chart can be copied.
Screenshots or video
https://github.com/visualize-admin/visualization-tool/assets/29289679/125e9a92-f6e6-4785-a4e1-9333fb64df89
Environment (please complete the following information):

Visualize environment and version:  Test v4.1
Browser and version Chrome

Additional context
Add any other context about the problem here.

@sosiology should be fixed on TEST :)

Can't reproduce anymore. Looks good to me 💯


uploadskeletons exptected input

hey @SridharJagannathan, I was wondering what is expected input to uploadskeletons function. I have a bunch of skeletons in swc format, that I read to navis:
swcs = navis.read_swc('data/')

But then:
uploadskeletons(swcs, skel_ids, skel_names, hostdirectory, 'longest_neurites')

Gives the following error:
AttributeError                            Traceback (most recent call last)
Input In [31], in <module>
----> 1 uploadskeletons(swcs, skel_ids, skel_names, hostdirectory, 'longest_neurites')

File ~/miniforge3/envs/newpyg2/lib/python3.9/site-packages/pyroglancer/skeletons.py:143, in uploadskeletons(skelsource, skelseglist, skelnamelist, path, layer_name)
    121 def uploadskeletons(skelsource, skelseglist, skelnamelist, path, layer_name):
    122     """Upload skeleton (of cloudvolume class) to a local server.
    123
    124     Parameters
   (...)
    140         object of cloudvolume class
    141     """
    142     info = {"@type": "neuroglancer_skeletons",
--> 143             "transform": skelsource[0].transform.flatten(),
    144             "vertex_attributes": [{"id": "radius", "data_type": "float32", "num_components": 1}],
    145             "scales": "um"}
    146     path = 'file://' + path + '/precomputed/' + layer_name
    147     cv = CloudVolume(path, info=info)

File ~/miniforge3/envs/newpyg2/lib/python3.9/site-packages/navis/core/skeleton.py:199, in TreeNeuron.__getattr__(self, key)
    193 """We will use this magic method to calculate some attributes on-demand."""
    194 # Note that we're mixing @property and __getattr__ which causes problems:
    195 # if a @property raises an Exception, Python falls back to __getattr__
    196 # and traceback is lost!
    197
    198 # Last ditch effort - maybe the base class knows the key?
--> 199 return super().__getattr__(key)

File ~/miniforge3/envs/newpyg2/lib/python3.9/site-packages/navis/core/base.py:156, in BaseNeuron.__getattr__(self, key)
    153             return 'NA'
    154     return None
--> 156 raise AttributeError(f'Attribute "{key}" not found')

AttributeError: Attribute "transform" not found

I tried to convert it to a regular list of TreeNeurons:
skel_list = [swcs[i] for i in range(len(swcs))]

But the error is the same. Any step that I'm missing?

Hi @dokato ,
For converting swc/treeneuron(s) to neuroglancer format, I have made an example here (look at the skeleton part.. section)
https://github.com/SridharJagannathan/pyroglancer/blob/master/pyroglancer/examples/generate_datasets/generate_hemibrain2flywire.ipynb
The basic strategy is that:

read the swc via navis (which you have already done like navis.read_swc)
next use the api to_ngskeletons whose input are treeneurons and returns a skelsource
next use the api to_precomputedskels whose input are skelsource and returns a skelsource
next use the api to_precomputedskelsinfo to set the info file to the host directory.

You can see in the example notebook, how i have fetched from hemibrain and then converted them to skeletons to display in flywire
let me know if it works?

Thanks @SridharJagannathan for a prompt reply. I moved on with your advise, but when I try to visualise the skeletons locally, I get:

I checked the paths, they look correct. However, comparing with meshes the precomputed file ids look weird:
fff14b96-d64e-449c-bd28-07787b7007db
fe44783e-e8b5-4db2-83c4-d0dc1031f00d

is this how it should be?

This must be some problem with default "skelsource" ids. When I modified them to:
start_idx = 1000
skelseglist_modified = []
for i in range(len(skelsource)):
    skelsource[i].id = str(start_idx + i)
    skelseglist_modified.append(str(start_idx + i))

All worked fine.

Yes, they are the problem sometimes when you convert using navis it returns such hexadecimal characters for the neuron ids (sometimes i think also for meshes), but the neuroglancer framework only accepts unit64, so convert those ids to the relevant body ids or some other number etc

or when you read the swc using navis you can give the id as input and read it out, so you can avoid this issue, see here for an example:
https://github.com/SridharJagannathan/pyroglancer/blob/0af3351e5f504f5d4feb9013cf5814c6032b6001/pyroglancer/tests/test_skeletons.py#L58-L59

I see, cool, cool. The most important that it works. Maybe worth adding a note to the docstrings. Or warning if possible to detect? Closing for now. Thanks for help again!


Namespaces

This pull request addresses #224 and to some extent #200.
Updated behavior of ns_dict and schemaloc_dict parameters for Entity.to_xml():

Namespace definitions (alias mappings) and schema locations that do not conflict with python-stix defaults are passed through to the document
Namespace alias mappings and schema location mappings which are already found in python-stix are ignored--python-stix values are used instead.

Looks good to me. :+1: for all the additional comments, which help me actually understand what the finalize method is doing. Thanks, @bworrell


Relative Z indexes do not carry through to scripts

Godot version:
3.2.3
OS/device including version:
Win 7
Issue description:
Relative Z-Indexes do not carry through to stand-alone script functions that are called from a parent.
This despite the fact that both the parent and the script have (default to) is_z_relative()== true`
Steps to reproduce:
Parent scene:
-> Background sprite (z-index 0)
-> A scene (z-index 100) with a script attached calls a stand alone script that has a _draw() call in it. That draw call will be placed underneath the background.
Minimal reproduction project:

Will produce if this is confirmed as a bug

@CitrusWire I don't understand exactly what's going on based on your description. Could you upload a minimal reproduction project, please?

@CitrusWire I don't understand exactly what's going on based on your description. Could you upload a minimal reproduction project, please?

Scene (a) has a script which calls Script (D). D calls the _draw() method and draws something onto screen. That something is below the background(Z), despite the fact that everything else in Scene (a) correctly draws on top of background(Z).

Scene (a) has a script which calls Script (D). D calls the _draw() method and draws something onto screen. That something is below the background(Z), despite the fact that everything else in Scene (a) correctly draws on top of background(Z).

In that case, I can confirm that _draw() doesn't take relative z into account (I ran into it once, before 3.2.3)

In that case, I can confirm that _draw() doesn't take relative z into account (I ran into it once, before 3.2.3)

https://user-images.githubusercontent.com/5731661/140613784-2b983964-b135-4b40-93da-a7133c6d7069.mp4
I've got a fuller explanation of how I'm seeing this issue in my small project in this video.
The fundamental issue is that there is no easy way, short of manually walking up the tree and adding up the z_index values to get the relative z depth in script. Even when chains of nodes all have Z is Relative set as true. I think an additional relative_z or similarly named property would be a good addition to expose this information.


operator image can't be pulled successfully

Describe the version
version about:

kubean
kubespray
kubenetes
what CNI and itsversion

Describe the bug
A clear and concise description of what the bug is.
The image  ghcr.io/kubean-io/kubean-operator:v0.2.5 can't be pulled in local.
How To Reproduce
Steps to reproduce the issue:
Install by helm chart.
Expected behavior
A clear and concise description of what you expected to happen.
The image should be pulled successfully.
We should use image registry to speed up.
Screenshots and log
If applicable, add screenshots and log to help explain your problem.
Additional context
Add any other context about the problem here.

/kind bug

i  meet this problem also in 0.2.7

It is not recommended to use the proxy registry to speed up, the proxy has a synchronization delay problem, there will be pull failure, if you want to use the proxy image address, you can specify through helm's --set parameter

 --set image.repository=ghcr.m.daocloud.io/kubean-io/kubean-operator to speedup pulling image


[[nodiscard]] warning on group

Hello. I understand from the documentation that it is most efficient to create groups upfront, before any entity is added to the registry. I guess this means adding a dummy call to registry::group like so
// initialise registry and other stuf...

r.group<A,B,C>();

// add entities, create world, etc...

// later:
for (auto e : r.group<A,B,C>()) {
    // do stuff
}

Is that right? If so, currently registry::group is marked as [[nodiscard]], which means the first line generates a warning. My understanding of [[nodiscard]] is that it should only be used if there is no good reason to ignore the return value (i.e., functions with no side effects) or when it is dangerous (i.e., likely to cause leaks etc.). But I don't think this is applicable in this case, because registry::group does have a side-effect (creating the group), hence it makes sense to call it and ignore the return value if we're only interested in the side-effect.

Fair enough. 🙂👍


[C++][Parquet] Add index pages to the format to support efficient page skipping to parquet-cpp

The scope of the Jira is to take advantage of indexes added as part of PARQUET-922
It is easier to implement this if we create sub-tasks
Reporter: Renato Javier Marroquín Mogrovejo / @renato2099
Assignee: Gang Wu](https://issues.apache.org/jira/secure/ViewProfile.jspa?name=wgtmac) / @wgtmac
Subtasks:

[X] Add Index support in the read path
[X] Add index support in the write path

Related issues:

[C++] Add support Parquet with Page Index (duplicates)

PRs and other links:

GitHub Pull Request #6807

Note: This issue was originally created as PARQUET-1404. Please see the migration documentation for further details.

Gang Wu / @wgtmac:
Hi [~encodedgeek], I will definitely go through your change as well as the review comments.
FYI, I have discussed the initiative with some maintainers of Apache Arrow. The code is running in production already so it is good time to contribute back to the community.

Arun Balajiee Lekshmi Narayanan:
Would you mind reviewing my changes with regards to issues before
proceeding?

Gang Wu / @wgtmac:
Hi @majetideepak, I am working on ARROW-10158 now, so I have changed the assignee to myself.
Also cc<EMAIL_ADDRESS>

Wes McKinney / @wesm:
Do you want to keep the discussion in one place, i.e. on the mailing list?

Gang Wu / @wgtmac:
Page index has been implemented in the parquet-cpp already. Close this.

Arun Balajiee Lekshmi Narayanan:
Interested to collaborate with you!

this page still claims that Column Index and Offset Index are not used during reading. Looking at https://github.com/apache/arrow/pull/6807/files as a layman it seems they are used.
Does the documentation need an update or am I reading the MR wrongly?

Just found this issue so it seems the docs are correct and it's indeed not used when reading - but lower level API support is present.


problem with one nimbleFunction using values from another

I am filing this as a note but will wait with details until devel is fixed and I can probe into it.

Drivel is fixed now.
What is the nature of this problem?
On Monday, November 14, 2016, perrydv<EMAIL_ADDRESS>wrote:

I am filing this as a note but will wait with details until devel is fixed
and I can probe into it.
—
You are receiving this because you are subscribed to this thread.
Reply to this email directly, view it on GitHub
https://github.com/nimble-dev/nimble/issues/245, or mute the thread
https://github.com/notifications/unsubscribe-auth/AETW4c9Rv79OfneuBpboEPeXxTNUnxbSks5q-NsegaJpZM4Kx3la
.

For the record, the problem occurred when nf2 uses an instance of nf1, but the user wants interfaces to both nf2 and nf1 from the compileNimble call, and nf2 is listed first in compileNimble.  In that case compilation of nf2 would trigger compilation of nf1, as it should, but then when nf1 is considered for compilation it accidentally instantiated a new object of nf1, resulting in two nf1 one objects when there should be the one nf1 object used by both nf2 and the user-interface to be returned for nf1.  I have fixed this.  It involves a new method promoteCallable in cppNimbleFunctionClass.  This is used when the first interface was a CmultiNimbleFunction interface (not very user-friendly), which will be replaced with a full interface (user friendly).  Also changes in cppProjectClass::compileNimbleFunction to avoid double instantiation and buildNimbleFxnInterface to allow passing of an external pointer to an already existing object.


Exporting Nested SFC's

At the moment I don't think you can export nested SFC's. This is useful because there are times when three components are. dependent on each other. How about a block for exporting components? Or you could use a special keyword for exporting them export.

Things that change together should live together.

Export block example
<export>
{ example }
</export>

<component name="example">
  <template>
  I'm component example.
  </template>
</component>

Export keyword example
<component name="example" export>
  <template>
  I'm component example.
  </template>
</component>

Thanks for the suggestion! Check out the latest release
Example usage:

<template>

        Hello, App!
        <HelloComponent />

</template>

<template>

        Hello, World!

</template>

<component name="hello-component" lang="vue">
    <template>

            Hello, Component!

    </template>
</component>

I don't think they should auto-export. But nice achievement.

I assume the template is the default one but the others are put in separate exports. automatically. I guess this means devs can choose between exporting multiple components or exporting one as the default and then others as components. The auto-exporting will cause some problems along the line. To prevent auto export there would have to be further nesting.


Add support for new localTime string values

coverage: 95.821% (+0.02%) from 95.806%
when pulling 6fcc948b5b2cc25a97a17193cfd77205045f9e4d on log0s:feature/add-localtime-strings
into e891bf7d28081c27b7376c54a0769fb6e9703548 on capellaspace:main.

Closing to redo this. Originally branched off the other feature branch, which means it's maintaining the old (pre-squash) commit history. Want to do this off main instead.


Add #BlackLivesMatter banner

This adds a banner to express community solidarity, like many Open Source projects including Ember, Django, TypeScript, React, Parcel, Angular or styled-components have added already.
The banner includes a link to the Equal Justice Initiative, inspired by React in particular.

On mobile views other than the landing page, the #BlackLivesMatter hashtag is included as part of the mobile bar and links to the top of the landing page where visitors can find the full banner including the donation link:

Thank you @jonaskuske!


Implement save feature for pytorch classifier

Now, save has been implemented for the Pytorch Classifier module and
it will save the optimizer and the model state dictionaries in the
specified path.  If not, it will save the above in the DATA_PATH
directory.
Description
This will now allow pytorch classifier's model's and optimizer's state dictionaries to be saved with the name and path specified.  They will be suffixed with '.optimizer' and '.model' appropriately.
Fixes # (issue)
Type of change
Please check all relevant options.

[ ] Bug fix (non-breaking)
[X ] New feature (non-breaking)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
[ ] This change requires a documentation update

Testing
Tried out the following and it works fine.
from art.classifiers import PyTorchClassifier
import torch
import torchvision.models as models
import torch.nn as nn
import torch.optim as optim

model = models.vgg11(pretrained=False)
loss = nn.MSELoss()
optimizer = optim.Adamax(model.parameters())

artClassifier = PyTorchClassifier((0,100), model, loss, optimizer, (1, 28, 28), 10)
artClassifier.save("dummy")
artClassifier.save("dummy",path="./weights")

Test Configuration:

OS Ubuntu 18.04.1 LTS
Python version
ART version or commit number : 120f95f
PyTorch 1.0.0

Checklist

[x] My code follows the style guidelines of this project
[x] I have performed a self-review of my own code
[x] I have commented my code
[ ] I have made corresponding changes to the documentation
[ ] My changes generate no new warnings
[ ] I have added tests that prove my fix is effective or that my feature works
[x] New and existing unit tests pass locally with my changes

Hi,
I've the following.
    def test_save(self):
        model = self.module_classifier
        import tempfile
        import os
        t_file = tempfile.NamedTemporaryFile()
        full_path = t_file.name
        t_file.close()
        base_name = os.path.basename(full_path)
        dir_name = os.path.dirname(full_path)
        model.save(base_name, path=dir_name)
        self.assertTrue(os.path.exists(full_path + ".optimizer"))
        self.assertTrue(os.path.exists(full_path + ".model"))
        os.remove(full_path + '.optimizer')
        os.remove(full_path + '.model')

I did run source run_tests.sh and it passed.  Does this look fine?

Test looks good! Waiting for Travis to finish the build, then I'll merge it.


[CELEBORN-666][REFACTOR] Unify exclude and blacklist related configurations and variables

What changes were proposed in this pull request?
Unify exclude and blacklist related configurations
Why are the changes needed?
Does this PR introduce any user-facing change?
How was this patch tested?

ping @waitinfuture @FMX @RexXiong @pan3793

The change makes sense to me, better to get approval by @waitinfuture before merging

I perfer merge https://github.com/apache/incubator-celeborn/pull/1588 before this PR


ELECTRON-967 (Handle network state and implement retry logic)

Description
Display a UI screen for network related issue instead of the blank screen ELECTRON-967
Solution Approach
Subscribe for did-fail-load and did-stop-loading webContents events to display an error message along with retrying network endpoint by fetching pod URL using electron-fetch module
Screencast

Related PRs
List related PRs against other branches/repositories:

branch
PR

3.x
link

QA Checklist

[X] Unit-Tests

[ ] Automation-Tests

@KiranNiranjan Please see my comments in the other PR and address them here as well.


Outbox pattern

### Tasks

Hello,
Is there any work being done on this topic? Have there been any decisions as to what to use for inter module communication? Given this repo is Azure heavy, would Azure service bus be the default?

Hello,
Is there any work being done on this topic? Have there been any decisions as to what to use for inter module communication? Given this repo is Azure heavy, would Azure service bus be the default?

Hi @ayuksekkaya
It will for sure be Azure Service Bus, and likely MassTransit for the .NET library. It's the only Azure Server that is listed that is not yet "implemented".
And for localhost, the plan is to use the new Azure Service Bus Emulator. It looks like there might be direct support for Aspire coming just around the corner: https://github.com/dotnet/aspire/pull/6737.
I would be happy to jump on a call and hear more about your needs and setup.

@tjementum ,
Yes MassTransit makes perfect sense.
I don't have any needs per se. I have been mostly studying and learning a lot from your code actually. And I wanted to see if I can contribute something as well. I was thinking maybe I can try working on some part of the integration events. I don't see a "tasks" list for this, so I wanted to see if you have something in local maybe.
I was thinking I guess a rough list of tasks is something like:

Add MassTransit library to SharedKernel. ( I am thinking both a worker service and the core project could be using MassTransit. Worker as the consumer, and the core as both consumer and publisher.)
Create a mapping between a domain event and an integration event. ( I am thinking that the sending of verification code email maybe would be the best place to start?)
Maybe add a new pipeline behavior for mapping and writing to MassTransit outbox using EF Core? Or maybe the existing PublishDomainEventsPipelineBehavior could be used?
Add Azure service bus to the bicep module, and the aspire host
Use it to communicate with other SCS?

What do you think? Did you have something completely different in mind? I can try reaching out to the email in your profile and we can set up some time.
I would love to help and learn from you!

Hi @ayuksekkaya,
Thank you for your interest in contributing to PlatformPlatform! Your enthusiasm and engagement are greatly appreciated.
PlatformPlatform serves as the foundation for a handful of companies I collaborate with daily. Many roadmap items, including this one, are researched and iterated on with these partners before implementation begins. The Outbox pattern, in particular, is not currently planned for further discovery or implementation in the coming months, but it is essentiel for a completed platform.
I am currently the main contributor and prefer to lead the implementation of major areas in the backend, DevOps, and cloud domains. Contributions are most welcome in areas like the frontend, where help is needed to build the world-class solutions, we aim for with PlatformPlatform.
While direct contributions to the core may not align at this time, feedback from those building solutions on top of PlatformPlatform would be invaluable. I’d be happy to offer a 1-hour intro call to help anyone get started.
Thank you again for your understanding and support!
Best regards,
Thomas


Error

It Said:[01:31:02.54] ERROR: Error: User not found.
(node:4536) UnhandledPromiseRejectionWarning: Error: User not found.
at Object.exports.makeTwitError (C:\Users\Downloads\Twitter-Monitor-master (7)\Twitter-Monitor-master\node_modules\twit\lib\helpers.js:74:13)
at onRequestComplete (C:\Users\Downloads\Twitter-Monitor-master (7)\Twitter-

Please refer to https://github.com/ezolla/Twitter-Monitor/issues/6 for the answer.


copy | paste to | from excel issues with dates

using this example: https://bossanova.uk/jexcel/examples/using-a-calendar-column-type
step 1 - to excel
in jexcel: i selected all 4 cells in the date column
in excel: pasted into excel.
result: excel swapped m/d and shrunk year to 2 digits.

excel shows 12/1/16, 12/2/16 ... where jexcel shows 01/12/2016, 02/12/2016 ...

step 2 - from excel
in excel: i moved the top date to the bottom and selected all 4 dates again
in jexcel: pasted into top date cell
result: jexcel didn't put any data in, if i start in a lower cell it expands downward with blank cells
expectation - need to be able to paste dates back and forth from excel without swapping day-month.
hopefully this isn't too hard.

related - pasting multiple cells from excel, the first date field is ending the paste operation.  that is most obvious when pasting into blank cells and subtlely wrong when pasting over old cells.
repro - copy all the cells from the initial calendar example to excel and paste back into new cells without the date column = :)  and with the date column = :(

paul - best part - works just fine from numbers.  gotta love excel.  or not.

jCalendar uses a fixed format 'yyyy-mm-dd hh:mm:ss' when you copy from jexcel you going to see that. This happens independent of what format the user is seeing. That's quite trick when you paste different formats on jexcel, since jexcel must receive the  'yyyy-mm-dd hh:mm:ss'.

wow that does sounds ugly.  unfortunately its the core use case i'm dealing with right now.  being able to copy several rows including dates from excel and paste into the web page and back again.

One easy solution is create a custom based date using any other datepicker plugin.

Maybe this one?
https://eonasdan.github.io/bootstrap-datetimepicker/

i'll have a look and let you know how it works.  being that it crashes the paste operation hopefully you can merge in and replace jcalendar...

Data is a little trickier since it can have different formats. Planning to take a look in the next version.


Patient Builder Improvements

Really good! Is there a way to reduce the amount of space each data element takes up? Most measures will have more than 10 elements, which makes the "see at a glance" a bit more difficult. The more we can show without scrolling, the better.

Would we want this to be the default view or would we still want to show the very minimal view, and people can choose to display the additional information?

and I agree, this looks really great!

I'd also be curious to see what this looks like on an actual test case for measure testing. Maybe they don't use nearly as many fields so it's a much more manageable view.

Future user stories include letting the user be able to filter/hide/show certain types of elements, sort elements, etc. With that additional level control, I think having this info displayed by default is OK.

Example of Duration

Codes display when minimized

Age now displays in Patient Builder

We recently got an email about needing to specify CPT codes in a very particular way in order to comply with a license agreement with AMA - so this may need to change. Don't know what that way is yet.
Also, it looks like the code numbers are no longer being displayed. I'd display the code number and the code description.

@edeyoung The code numbers are still being displayed when the data criteria is open, but not when it is closed. Should I change it to include, 1. Codeset, 2. Code Number, and 3. Code Description when open?

Everything should display when open (code set, number, description). When closed, I think it should either display code set and number (to save space) or code set, number, and description. In all cases, the code number should be shown.

In all cases, the code number should be shown.

@edeyoung I'm not sure the code number should get such priority. We've heard from users that that number is more noise than signal.
If we do include the number (when closed), maybe we can omit the code set - if the associated code set can be inferred from the length/format/value of the number.

I know the Cypress folks were specifically interested in the description, but I didn't hear that they weren't interested in the code. Has this come up in other conversations also?
I'm concerned about omitting a piece of data that the users have become accustomed to seeing.

I agree with @edeyoung, the code can be helpful when debugging a test cases when the cause is value set related.

Please note that closed criteria showed no information on codes, so this isn't "omitting" anything that was previously available.

ah - wasn't thinking of the closed criteria. When open, it used to show codes only. I'm OK with not showing anything related to the code when the data criterion is closed. Usually I'm looking for attributes and date/time stamps.

I think it's fine to not show anything about codes in the closed view if it doesn't make sense from a user perspective. If we are going to show code-related information, I think we should show the code number to maintain consistency and utility with what they're familiar with.

Here's a side-by-side of without code number and with code number. Thoughts?

With the code. But I think Rute makes a good point - maybe we don't need the codes in the summary view at all.
What about losing the space between the codeset and the code?
e.g.: CPT:99365 - Initial comprehensive...

What do you think??

Looks good!!!
The "Show More" alignment seems to be a bit off.

Examples of Age spanning Measure Period

Multiple Show More Buttons now format properly

Data Criteria Turns Red When Something is Not Performed

Changed the Way Age and Durations are Displayed

Changed the Red to Yellow http://puu.sh/pSnvJ/19d4c592b3.png

Changed the location of the "Utility" Buttons, and added two new buttons

The new interface is definitely an improvement, but there are still a few things that feel odd about it from a design perspective, that I think make the interface feel less cohesive than it could be:

The SHOW INFORMATION / HIDE INFORMATION button still feels like it's in an odd place; some controls are at the top of the PATIENT HISTORY and some are at the bottom of the ELEMENTS, what's the reasoning behind the placements? In particular, when there are a lot of elements, a user would have to scroll to find that button.

There are three different controls that basically toggle between two states (SHOW INFORMATION / HIDE INFORMATION, OPEN ALL / CLOSE ALL, and ORDER BY DATA / ORDER BY ELEMENTS) and three different mechanisms that get used to do the toggling (a button that changes, two buttons to click that don't show state, and two buttons to click that do show state).

Only the OPEN ALL and CLOSE ALL are behind the gear, it seems off that some items are behind a gear and some (ORDER BY) are not.

What does ORDER BY "ELEMENTS" mean? Alphabetical order by name? If so, why not just say "NAME"?

The location of the age language seems a bit odd, it's wedged over to the right with the living status:

Seems like it would make more sense to include it under the birth date, since it's usually more closely related to that data, plus it makes better use of vertical real-estate:

Having weird computer issues, so pushing up changes now - No need to review these currently, many things still incomplete

@pkmitre I'd like to sit down with you and go over some of your comments/ask a few questions before you take another look

"End Date precedes Start Date" seems to be shown on a data element even when the end date is set equal to the start date

I think the measure request to include icons for outdated codes might be a good opportunity to merge this PR in.
I think some of the UI should change slightly. See my screen shot below and the specific changes I'm interested in:

Make "Order By" a drop down box in case we want to add more options. Also, less visual clutter.
Remove "collapse all" and "expand all". With the preview information, this seems like less of a necessity.
Change wording and placement of the "show details" check box.
Include codes in the detail view.
(in expanded view) include time duration

Oh god did I just include all the commits from everything...Should I cherry pick just mine, and worry about merging/git issues later?

I think cherry picking does seem wise!

Cherrypicked the correct commits, and force pushed to remove any unwanted commits in this PR.


CollectiveAllReduceStrategy fails with CPU-only workers

System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow): no
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): macOS High Sierra
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: N/A
TensorFlow installed from (source or binary): tf-nightly
TensorFlow version (use command below): ('v1.9.0-rc2-4081-g626bc997c2', '1.11.0-dev20180913')
Python version: Python 2.7.15
Bazel version (if compiling from source): N/A
GCC/Compiler version (if compiling from source): N/A
CUDA/cuDNN version: N/A
GPU model and memory: N/A
Exact command to reproduce: https://gist.github.com/df3df82f7ae8f47b6288fc42eb8c8b17

Describe the problem
Invoke tf.estimator.train_and_evaluate with CollectiveAllReduceStrategy fails on CPU-only worker nodes, with the following message:
InternalError: ScopedAllocatorMgr not supported on device /job:worker/replica:0/task:0/device:CPU:0

Source code / logs
from tensorflow.contrib.distribute import CollectiveAllReduceStrategy
from tensorflow.contrib.distribute import DistributeConfig

distribution = CollectiveAllReduceStrategy(num_gpus_per_worker=0)

config = tf.estimator.RunConfig(
    experimental_distribute=DistributeConfig(
        train_distribute=distribution,
        remote_cluster={
            'worker': ['localhost:5000', 'localhost:5001'],
        },
    )
)

estimator = tf.estimator.Estimator(model_fn=model_fn, config=config)

tf.estimator.train_and_evaluate(estimator=estimator, train_spec=train_spec, eval_spec=eval_spec)

INFO:tensorflow:CollectiveAllReduceStrategy with local_devices = ['/device:CPU:0']
INFO:tensorflow:Initializing RunConfig with distribution strategies.
INFO:tensorflow:RunConfig initialized for Distribute Coordinator with STANDALONE_CLIENT mode
WARNING:tensorflow:Using temporary folder as model directory: /var/folders/gn/sjntndrs1fs22kfr302697mr0000gn/T/tmpeIE_x6
INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_num_ps_replicas': 0, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_global_id_in_cluster': 0, '_is_chief': True, '_cluster_spec': {'worker': ['localhost:5000', 'localhost:5001']}, '_model_dir': '/var/folders/gn/sjntndrs1fs22kfr302697mr0000gn/T/tmpeIE_x6', '_protocol': None, '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_tf_random_seed': None, '_save_summary_steps': 100, '_device_fn': None, '_experimental_distribute': DistributeConfig(train_distribute=<tensorflow.contrib.distribute.python.collective_all_reduce_strategy.CollectiveAllReduceStrategy object at 0x123f09810>, eval_distribute=None, remote_cluster={'worker': ['localhost:5000', 'localhost:5001']}), '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_evaluation_master': '', '_eval_distribute': None, '_train_distribute': <tensorflow.contrib.distribute.python.collective_all_reduce_strategy.CollectiveAllReduceStrategy object at 0x123f09810>, '_master': '', '_distribute_coordinator_mode': 'standalone_client'}
INFO:tensorflow:Running `train_and_evaluate` with Distribute Coordinator.
INFO:tensorflow:Running Distribute Coordinator with mode = 'standalone_client', cluster_spec = {'worker': ['localhost:5000', 'localhost:5001']}, task_type = None, task_id = None, environment = None, rpc_layer = 'grpc'
WARNING:tensorflow:`eval_strategy` is not passed in. No distribution strategy will be used for evaluation.
INFO:tensorflow:Multi-worker CollectiveAllReduceStrategy with cluster_spec = {'worker': ['localhost:5000', 'localhost:5001']}, task_type = 'worker', task_id = 0, num_workers = 2, local_devices = ['/job:worker/task:0']
INFO:tensorflow:Multi-worker CollectiveAllReduceStrategy with cluster_spec = {'worker': ['localhost:5000', 'localhost:5001']}, task_type = 'worker', task_id = 1, num_workers = 2, local_devices = ['/job:worker/task:1']
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Collective All-reduce invoked with batches size = 2, num_workers = 2
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Creating chief session creator with config: device_filters: "/job:worker/task:0"
allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
    scoped_allocator_optimization: ON
    scoped_allocator_opts {
      enable_op: "CollectiveReduce"
    }
  }
}
isolate_session_state: true
experimental {
  collective_group_leader: "/job:worker/replica:0/task:0"
}

INFO:tensorflow:Collective All-reduce invoked with batches size = 2, num_workers = 2
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Creating chief session creator with config: device_filters: "/job:worker/task:1"
allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
    scoped_allocator_optimization: ON
    scoped_allocator_opts {
      enable_op: "CollectiveReduce"
    }
  }
}
isolate_session_state: true
experimental {
  collective_group_leader: "/job:worker/replica:0/task:0"
}

INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Initialize system
INFO:tensorflow:Initialize system
INFO:tensorflow:Saving checkpoints for 0 into /var/folders/gn/sjntndrs1fs22kfr302697mr0000gn/T/tmpeIE_x6/model.ckpt.

Exception in thread Thread-5:
Traceback (most recent call last):
  File<EMAIL_ADDRESS>line 801, in __bootstrap_inner
    self.run()
  File<EMAIL_ADDRESS>line 754, in run
    self.__target(*self.__args, **self.__kwargs)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/distribute/distribute_coordinator.py", line 344, in _run_single_worker
    worker_fn(strategy)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/distribute/estimator_training.py", line 232, in _worker_fn
    hooks=list(train_spec.hooks))
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 355, in train
    loss = self._train_model(input_fn, hooks, saving_listeners)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 1178, in _train_model
    return self._train_model_distributed(input_fn, hooks, saving_listeners)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 1325, in _train_model_distributed
    saving_listeners)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 1408, in _train_with_estimator_spec
    _, loss = mon_sess.run([estimator_spec.train_op, estimator_spec.loss])
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 671, in run
    run_metadata=run_metadata)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 1148, in run
    run_metadata=run_metadata)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 1239, in run
    raise six.reraise(*original_exc_info)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 1224, in run
    return self._sess.run(*args, **kwargs)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 1296, in run
    run_metadata=run_metadata)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 1076, in run
    return self._sess.run(*args, **kwargs)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 887, in run
    run_metadata_ptr)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1110, in _run
    feed_dict_tensor, options, run_metadata)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1286, in _do_run
    run_metadata)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1306, in _do_call
    raise type(e)(node_def, op, message)
InternalError: ScopedAllocatorMgr not supported on device /job:worker/replica:0/task:1/device:CPU:0
	 [[{{node scoped_allocator_1}} = _ScopedAllocator[T=DT_FLOAT, expected_call_count=2, id=1, sa_name="scoped_allocator_1", shape=[17], shapes=[[1,1], [1]], _device="/job:worker/replica:0/task:1/device:CPU:0"]()]]

Exception in thread Thread-4:
Traceback (most recent call last):
  File<EMAIL_ADDRESS>line 801, in __bootstrap_inner
    self.run()
  File<EMAIL_ADDRESS>line 754, in run
    self.__target(*self.__args, **self.__kwargs)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/distribute/distribute_coordinator.py", line 344, in _run_single_worker
    worker_fn(strategy)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/distribute/estimator_training.py", line 232, in _worker_fn
    hooks=list(train_spec.hooks))
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 355, in train
    loss = self._train_model(input_fn, hooks, saving_listeners)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 1178, in _train_model
    return self._train_model_distributed(input_fn, hooks, saving_listeners)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 1325, in _train_model_distributed
    saving_listeners)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 1408, in _train_with_estimator_spec
    _, loss = mon_sess.run([estimator_spec.train_op, estimator_spec.loss])
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 671, in run
    run_metadata=run_metadata)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 1148, in run
    run_metadata=run_metadata)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 1239, in run
    raise six.reraise(*original_exc_info)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 1224, in run
    return self._sess.run(*args, **kwargs)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 1296, in run
    run_metadata=run_metadata)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 1076, in run
    return self._sess.run(*args, **kwargs)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 887, in run
    run_metadata_ptr)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1110, in _run
    feed_dict_tensor, options, run_metadata)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1286, in _do_run
    run_metadata)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1306, in _do_call
    raise type(e)(node_def, op, message)
InternalError: ScopedAllocatorMgr not supported on device /job:worker/replica:0/task:0/device:CPU:0
	 [[{{node scoped_allocator_1}} = _ScopedAllocator[T=DT_FLOAT, expected_call_count=2, id=1, sa_name="scoped_allocator_1", shape=[17], shapes=[[1,1], [1]], _device="/job:worker/replica:0/task:0/device:CPU:0"]()]]

PS: thanks @yuefengz for today's introduction of multi-node distribution strategy in TF Roadshow 2018@Beijing 😆

Looks like your CPU device is not overriding the
virtual ScopedAllocatorMgr* GetScopedAllocatorMgr() const { return nullptr; }
method on DeviceBase.  It is overridden in common_runtime/threadpool_device.h, which is normally the only way of building a CPU device.   I'm not sure whether this is an issue with the MacOS build or something specific to your build.  Given some more time, I'll try to reproduce.

@byronyi really appreciate you came to our roadshow event!
For now, if there is not a simple workaround, you can try MirroredStrategy as well. But we still recommend CollectiveAllReducStrategy for multi-node training and we will fix it as soon as possible. Thanks!

It seems I used SingleThreadedCpuDevice that was added in 9b18bd70b5739d646b21b7d45de0e5c96b8cc2a1, later than ScopedAllocator, which was added in 282750fee5e2df502436ca9ef6a95283f8adab34.
@skye Mind to fix this? Or I could help submit a patch if you're okay with it.

@byronyi if you'd like to submit a PR that'd be great!

@poxvoculi @dubey PR submitted.

@yuefengz I just tried MirroredStrategy but it seems require NCCL even if it is a CPU-only build.
Is this going to be fixed soon? If not, I will open a separate issue for this.
INFO:tensorflow:Initializing RunConfig with distribution strategies.
INFO:tensorflow:RunConfig initialized for Distribute Coordinator with STANDALONE_CLIENT mode
WARNING:tensorflow:Using temporary folder as model directory: /var/folders/gn/sjntndrs1fs22kfr302697mr0000gn/T/tmpSuQoQy
INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_num_ps_replicas': 0, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_global_id_in_cluster': 0, '_is_chief': True, '_cluster_spec': {'ps': ['localhost:5000'], 'worker': ['localhost:5001']}, '_model_dir': '/var/folders/gn/sjntndrs1fs22kfr302697mr0000gn/T/tmpSuQoQy', '_protocol': None, '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_tf_random_seed': None, '_save_summary_steps': 100, '_device_fn': None, '_experimental_distribute': DistributeConfig(train_distribute=<tensorflow.contrib.distribute.python.mirrored_strategy.MirroredStrategy object at 0x12316fc90>, eval_distribute=None, remote_cluster={'ps': ['localhost:5000'], 'worker': ['localhost:5001']}), '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_evaluation_master': '', '_eval_distribute': None, '_train_distribute': <tensorflow.contrib.distribute.python.mirrored_strategy.MirroredStrategy object at 0x12316fc90>, '_master': '', '_distribute_coordinator_mode': 'standalone_client'}
INFO:tensorflow:Running `train_and_evaluate` with Distribute Coordinator.
INFO:tensorflow:Running Distribute Coordinator with mode = 'standalone_client', cluster_spec = {'ps': ['localhost:5000'], 'worker': ['localhost:5001']}, task_type = None, task_id = None, environment = None, rpc_layer = 'grpc'
WARNING:tensorflow:`eval_strategy` is not passed in. No distribution strategy will be used for evaluation.
WARNING:tensorflow:Could not find a standard reader in the input pipeline(one of TextLineDataset, TFRecordDataset, FixedLengthRecordDataset).So auto-sharding is not done. Please verify correctness of auto-sharding for your input.
WARNING:tensorflow:Could not find a standard reader in the input pipeline(one of TextLineDataset, TFRecordDataset, FixedLengthRecordDataset).So auto-sharding is not done. Please verify correctness of auto-sharding for your input.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:batch_all_reduce invoked for batches size = 2 with algorithm = nccl, num_packs = 1, agg_small_grads_max_bytes = 0 and agg_small_grads_max_group = 10
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Creating chief session creator with config: allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
isolate_session_state: true

INFO:tensorflow:Graph was finalized.
---------------------------------------------------------------------------
NotFoundError                             Traceback (most recent call last)
<ipython-input-6-77bd9e8610db> in <module>()
     17 train_spec = tf.estimator.TrainSpec(input_fn=input_fn)
     18 eval_spec = tf.estimator.EvalSpec(input_fn=input_fn)
---> 19 tf.estimator.train_and_evaluate(estimator=estimator, train_spec=train_spec, eval_spec=eval_spec)

/usr/local/lib/python2.7/site-packages/tensorflow/python/estimator/training.pyc in train_and_evaluate(estimator, train_spec, eval_spec)
    460     logging.info('Running `train_and_evaluate` with Distribute Coordinator.')
    461     distribute_coordinator_training.train_and_evaluate(
--> 462         estimator, train_spec, eval_spec, _TrainingExecutor)
    463     return
    464

/usr/local/lib/python2.7/site-packages/tensorflow/python/distribute/estimator_training.pyc in train_and_evaluate(estimator, train_spec, eval_spec, executor_cls)
    262       mode=run_config._distribute_coordinator_mode,
    263       cluster_spec=cluster_spec,
--> 264       session_config=run_config.session_config)

/usr/local/lib/python2.7/site-packages/tensorflow/python/distribute/distribute_coordinator.pyc in run_distribute_coordinator(worker_fn, strategy, eval_fn, eval_strategy, mode, cluster_spec, task_type, task_id, session_config, rpc_layer)
    747       else:
    748         _run_in_graph_client(worker_fn, strategy, eval_fn, eval_strategy,
--> 749                              cluster_spec, session_config, rpc_layer)
    750     else:
    751       # If not a client job, run the standard server.

/usr/local/lib/python2.7/site-packages/tensorflow/python/distribute/distribute_coordinator.pyc in _run_in_graph_client(worker_fn, strategy, eval_fn, eval_strategy, cluster_spec, session_config, rpc_layer)
    471       None,
    472       session_config,
--> 473       rpc_layer=rpc_layer)
    474   if eval_thread:
    475     eval_thread.join()

/usr/local/lib/python2.7/site-packages/tensorflow/python/distribute/distribute_coordinator.pyc in _run_single_worker(worker_fn, strategy, cluster_spec, task_type, task_id, session_config, rpc_layer, worker_barrier)
    342       worker_barrier=worker_barrier)
    343   with context:
--> 344     worker_fn(strategy)
    345
    346

/usr/local/lib/python2.7/site-packages/tensorflow/python/distribute/estimator_training.pyc in _worker_fn(strategy)
    230         input_fn=train_spec.input_fn,
    231         max_steps=train_spec.max_steps,
--> 232         hooks=list(train_spec.hooks))
    233
    234   def _eval_fn(strategy):

/usr/local/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.pyc in train(self, input_fn, hooks, steps, max_steps, saving_listeners)
    353
    354       saving_listeners = _check_listeners_type(saving_listeners)
--> 355       loss = self._train_model(input_fn, hooks, saving_listeners)
    356       logging.info('Loss for final step: %s.', loss)
    357       return self

/usr/local/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.pyc in _train_model(self, input_fn, hooks, saving_listeners)
   1176   def _train_model(self, input_fn, hooks, saving_listeners):
   1177     if self._train_distribution:
-> 1178       return self._train_model_distributed(input_fn, hooks, saving_listeners)
   1179     else:
   1180       return self._train_model_default(input_fn, hooks, saving_listeners)

/usr/local/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.pyc in _train_model_distributed(self, input_fn, hooks, saving_listeners)
   1323         return self._train_with_estimator_spec(estimator_spec, worker_hooks,
   1324                                                hooks, global_step_tensor,
-> 1325                                                saving_listeners)
   1326
   1327   def _train_with_estimator_spec(self, estimator_spec, worker_hooks, hooks,

/usr/local/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.pyc in _train_with_estimator_spec(self, estimator_spec, worker_hooks, hooks, global_step_tensor, saving_listeners)
   1403         save_summaries_steps=self._config.save_summary_steps,
   1404         config=self._session_config,
-> 1405         log_step_count_steps=self._config.log_step_count_steps) as mon_sess:
   1406       loss = None
   1407       while not mon_sess.should_stop():

/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in MonitoredTrainingSession(master, is_chief, checkpoint_dir, scaffold, hooks, chief_only_hooks, save_checkpoint_secs, save_summaries_steps, save_summaries_secs, config, stop_grace_period_secs, log_step_count_steps, max_wait_secs, save_checkpoint_steps, summary_dir)
    449         max_wait_secs=max_wait_secs,
    450         save_checkpoint_steps=save_checkpoint_steps,
--> 451         summary_dir=summary_dir)
    452
    453   if not is_chief:

/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in _create_monitored_session_with_worker_context(worker_context, scaffold, checkpoint_dir, hooks, chief_only_hooks, save_checkpoint_secs, save_summaries_steps, save_summaries_secs, config, stop_grace_period_secs, log_step_count_steps, max_wait_secs, save_checkpoint_steps, summary_dir)
    340       session_creator=session_creator,
    341       hooks=all_hooks,
--> 342       stop_grace_period_secs=stop_grace_period_secs)
    343
    344

/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in __init__(self, session_creator, hooks, stop_grace_period_secs)
    919     super(MonitoredSession, self).__init__(
    920         session_creator, hooks, should_recover=True,
--> 921         stop_grace_period_secs=stop_grace_period_secs)
    922
    923

/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in __init__(self, session_creator, hooks, should_recover, stop_grace_period_secs)
    641         stop_grace_period_secs=stop_grace_period_secs)
    642     if should_recover:
--> 643       self._sess = _RecoverableSession(self._coordinated_creator)
    644     else:
    645       self._sess = self._coordinated_creator.create_session()

/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in __init__(self, sess_creator)
   1105     """
   1106     self._sess_creator = sess_creator
-> 1107     _WrappedSession.__init__(self, self._create_session())
   1108
   1109   def _create_session(self):

/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in _create_session(self)
   1110     while True:
   1111       try:
-> 1112         return self._sess_creator.create_session()
   1113       except _PREEMPTION_ERRORS as e:
   1114         logging.info('An error was raised while a session was being created. '

/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in create_session(self)
    798       """Creates a coordinated session."""
    799       # Keep the tf_sess for unit testing.
--> 800       self.tf_sess = self._session_creator.create_session()
    801       # We don't want coordinator to suppress any exception.
    802       self.coord = coordinator.Coordinator(clean_stop_exception_types=[])

/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in create_session(self)
    564         init_op=self._scaffold.init_op,
    565         init_feed_dict=self._scaffold.init_feed_dict,
--> 566         init_fn=self._scaffold.init_fn)
    567
    568

/usr/local/lib/python2.7/site-packages/tensorflow/python/training/session_manager.pyc in prepare_session(self, master, init_op, saver, checkpoint_dir, checkpoint_filename_with_path, wait_for_checkpoint, max_wait_secs, config, init_feed_dict, init_fn)
    285                            "init_fn or local_init_op was given")
    286       if init_op is not None:
--> 287         sess.run(init_op, feed_dict=init_feed_dict)
    288       if init_fn:
    289         init_fn(sess)

/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict, options, run_metadata)
    885     try:
    886       result = self._run(None, fetches, feed_dict, options_ptr,
--> 887                          run_metadata_ptr)
    888       if run_metadata:
    889         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _run(self, handle, fetches, feed_dict, options, run_metadata)
   1108     if final_fetches or final_targets or (handle and feed_dict_tensor):
   1109       results = self._do_run(handle, final_targets, final_fetches,
-> 1110                              feed_dict_tensor, options, run_metadata)
   1111     else:
   1112       results = []

/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1284     if handle is None:
   1285       return self._do_call(_run_fn, feeds, fetches, targets, options,
-> 1286                            run_metadata)
   1287     else:
   1288       return self._do_call(_prun_fn, handle, feeds, fetches)

/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _do_call(self, fn, *args)
   1304           pass
   1305       message = error_interpolation.interpolate(message, self._graph)
-> 1306       raise type(e)(node_def, op, message)
   1307
   1308   def _extend_graph(self):

NotFoundError: Op type not registered 'NcclAllReduce' in binary running on Bairens-MacBook.local. Make sure the Op and Kernel are registered in the binary running in this process. Note that if you are loading a saved graph which used ops from tf.contrib, accessing (e.g.) `tf.contrib.resampler` should be done before importing the graph, as contrib ops are lazily registered when the module is first accessed.

@byronyi would be good to file a separate issue for MirroredStrategy, we should not default to nccl for CPU only builds.

I guess #21470 would be enough for now? I saw multiple issues opened for dist-strat, let's not overload @yuefengz at this moment :)

@byronyi it uses nccl as default all-reduce operations. The simplest workaround it to install nccl, or specifiy cross_tower_ops=tf.contrib.distribute.ReductionToOneDeviceCrossTowerOps in MirroredStrategy. If you have multiple workers in your cluster, MirroredStrategy will switch to another cross_tower_ops. Would that work for you?
We probably can check whether nccl is installed and if not switch to ReductionToOneDeviceCrossTowerOps in the future.


Running boot2docker ip inside boot2docker shell in windows returns not fo...

...und error, let's change it.

Can you please sign your commits following these rules:
https://github.com/docker/docker/blob/master/CONTRIBUTING.md#sign-your-work
The easiest way to do this is to amend the last commit:
$ git clone -b "patch-1" git@github.com:jaycode/docker.git somewhere
$ cd somewhere
$ git commit --amend -s --no-edit
$ git push -f

This will update the existing PR, so you do not need to open a new one.

ping @jaycode Would you like us to pick this up for you?

Hi, yes please, I got my hands tied on another project atm. Great job with
docker btw love it so much!
On Apr 3, 2015 8:40 PM, "moxiegirl"<EMAIL_ADDRESS>wrote:

ping @jaycode https://github.com/jaycode Would you like us to pick this
up for you?
—
Reply to this email directly or view it on GitHub
https://github.com/docker/docker/pull/11925#issuecomment-89290579.

Thanks @jaycode! Carried in #12057 (ping @moxiegirl @fredlf)


Update npm packages

Make versions less obscure
Move jasmine-jquery to node_modules instead of keeping it in the repo
Update Gruntfile.js

1 fail then :+1:


Emails service with support for enqueue jobs

related: https://github.com/kamilkisiela/graphql-hive/issues/179

Email org's owner about the rate-limit
Schema


Serverless URL Mapping Error when 2 Flex Project Templates are running on a single Flex instance

Setup
Single Flex Instance running the following:

Vanilla Flex Project Template (Deployed using Github Actions)
Custom Flex Project Template with "removed features" and "rename template" - i.e. standalone features built using Flex Project Template structure

Issue
Flex plugin not able to read the correct serverless URL using "this.serverlessDomain". It defaults to the "custom-flex-extensions-serverless" which is the serverless URL that is created with the original Flex Project Template.
Potential Solution
When running "rename template", we need to modify the following:

"scripts/config/mappings.json" - SERVERLESS_DOMAIN
"flex-template-XXX/src/utils/serverless/ApiService/index.ts" Ensure the "custom_data" portion is renamed and not appended
terraform scripts serverless domain lookup should user the new domain name

Hi @leroychan
I added a PR to address this and #379 - i wasn't clear on point 2 above though - i believe its addressed but would like to confirm with you.


DateTime Filters not working

Hello,
We're running the HotChocolate PRISMA-like Filters on top of a MongoDB.
We're currently experiencing troubles with filters on DateTimeType Fields.
To Reproduce
A sample repository is available here:
https://github.com/gmiserez/HotChocolate_DateTimeFilterIssues
Here are our queries:
query allConcerts
{
  blackSabbath2019Tour {
    concertDate
    name
  }
}
query allConcertsOf2019
{
  blackSabbath2019Tour(where: { concertDate_gte: "2019-06-01T08:00:00.000Z" }) {
    concertDate
    name
  }
}
query allConcertsOnExactDate
{
  blackSabbath2019Tour(where: {concertDate: "2019-06-01T08:00:00.000Z"}){
    concertDate
    cityName: name
  }
}

The first query returns this:
  "data": {
    "blackSabbath2019Tour": [
      {
        "concertDate": "2019-06-01T08:00:00.000Z",
        "name": "Amsterdam"
      },
      {
        "concertDate": "2019-07-10T08:00:00.000Z",
        "name": "Berlin"
      },
      {
        "concertDate": "2019-06-18T08:00:00.000Z",
        "name": "Paris"
      },
      {
        "concertDate": "2019-03-15T08:00:00.000Z",
        "name": "Zürich"
      }
    ]

The second query returns an error:

The binary operator GreaterThanOrEqual is not defined for the types 'System.DateTime' and 'System.DateTimeOffset'.

Third query:

The binary operator Equal is not defined for the types 'System.DateTime' and 'System.DateTimeOffset'.

Expected behavior
Ideally it should be possible to filter on DateTime fields. However I don't know if this can be fixed in HotChocolate or if this issue is with the MongoApi itself.
Packages:

HotChocolate.* 11.0.0-preview.4
MongoDB.Driver 2.8.1

I had this same issue. I ended up changing DateTimeType to DateType for my date field. In your sample's CityType, you could change the type to be DateType.
descriptor.Field(c => c.ConcertDate).Type<NonNullType<DateType>>();
The DateTimeType uses the CLR type System.DateTimeOffset, whereas the DateType uses System.DateTime.
I think the cause is confusion between the naming of DateType vs DateTimeType. I would expect DateTimeType to use the CLR type System.DateTime. Is this naming intentional?

This one works now in V11 and V10.
#1170


Performance via referential equality on useForm functions

Are you submitting a bug report or a feature request?
feature request (or more like a performance request)
What is the current behavior?
onBlur, onChange and onFocus from useForm(...).input do not maintain referential equality between renders. Therefore React.memo is hard to correctly implement.
What is the expected behavior?
Preferably the aforementioned functions (onBlur, onChange and onFocus) would be referentially equal between renders.
What's your environment?
"react-dom": "16.8.2"
"final-form": "^4.11.1"
"react-final-form-hooks": "^1.0.0"

Other information

Hi,
This is something I'm interested in writing a PR on. But I'm finding it difficult to properly setup a development environment. Are there any dev-docs I can reference?

Great insight. 👍
The dev stuff can be hard, as we currently have no test suite. I've had some success in the past consuming a library as I write it using yalc. Good luck? 😄

Good luck?

😆


This page should provide packing rules of bit-fields IMO

In tests I have found that:
Only contiguous bit-fields whose types are of the same width may be packed into an allocation unit (e.g. two bit-fields of type int and unsigned long respectively may be packed together). The allocation unit is of the size and alignment as types of such bit-fields. Packing is greedy and from left to right. The total bit width of packed bit-fields is as large as possible, but must not greater than the bit width of the allocation unit.
These details are implementation-defined in C/C++ standard, but not documented here  (and on https://docs.microsoft.com/en-us/cpp/c-language/c-bit-fields?view=vs-2019) currently.
I don't know whether the conclusion I got is right. The packing rules should be documented IMO, since some codes may depend on it.

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: a78c89c2-7d34-fac8-9043-4099bc8637dd
Version Independent ID: e265dccc-e3f6-7464-a6cf-e0e19aea772f
Content: C++ Bit Fields
Content Source: docs/cpp/cpp-bit-fields.md
Product: visual-cpp
Technology: cpp-language
GitHub Login: @corob-msft
Microsoft Alias: corob

Thank you for your patience as we work through our backlog. We have received more issues than we can manage at this time. We are closing issues based on their severity, how broadly applicable they are, and whether there is enough information provided to act on the issue. If you believe that this issue should not have been closed, please add a comment explaining why by 3/8/2024. We appreciate your contribution and encourage you to propose changes using public PRs, or submit feedback through our updated control at the top of the page, as we phase out Git Issues for this repo. For more information on our feedback systems, see https://aka.ms/ContentUserFeedback.


fixing issue that prevents more than one test using Router

Moving beforeEachProviders() from global scope, where it only makes sense to have one, to inside the describe. Then other tests can have their own beforeEachProviders() inside their describes and Jasmine will take care of cleaning up the installed providers after the describe has been run.

Looks good, thanks!


An error warning into typescript

I point out that if not saved as a dev we can find an error warning into typescript:
path: ~\node_modules\html-express-js\src\index.js
line 44: const includeFilePaths = await glob(${includesDir}/*.js);
Result: "Expected 0 arguments, but got 1.ts(2554)"

Hey, thanks for opening issue. What do you mean by "saved as a dev"? Could you also include code demonstrating how you're using the package and what node version, so I can reproduce? It would help me identify the issue you're seeing.

Hello! thank you for make this extension!
when install it in nodejs:
>_ npm i -D  html-express-js
or:
>_ npm install -save-dev html-express-js
but i have notice now that problem appear in any case.
I thinked it is resolved but no, my bad. in any case, if you are in vscode  and open that index have that warning flag
```
Expected 0 arguments, but got 1.ts(2554)
const includesDir: any"
```

Hey thanks for filing! I'm going to close this since I'm unable to reproduce this. But feel free to reopen and describe the steps to reproduce if this is still happening for you.


[Zotero] Add an open pdf action.

Extension – Zotero

Author: @sadanand-singh

Describe the feature and the current behavior/state.
Can you add an action to open pdf file directly?
Thank you for creating zotero and notion extensions! They are really helpful to research!
Who will benefit from this feature?
Zotero users
Any Other info.
https://forums.zotero.org/discussion/86980/quickly-create-a-open-pdf-link
This link may help.

Thanks @wooogler! I had thought about adding that, but the issue is that for open-pdf links we would need the item key of the pdf not the actual parent reference. I was not able to find a way to find the item key of the pdf from the API. See the last comment in the above forum. If you are able to find a way to get this, please let me know, I would be happy to implement this quickly.

@wooogler Thanks for the suggestion. I have found a way to implement this. See PR #956.


[CPP] Fixed a avg with groupby issue, now Q17 is correct

@xuechendi
Would you  add the prefix for every PR‘s  title？ For example : [oap-native-sql]

@xuechendi
Would you add the prefix for every PR‘s title？ For example : [oap-native-sql]

@zhixingheyi-tian , sure, will follow this naming pattern in future. Thanks


fix the import path of msda

Motivation
In https://github.com/open-mmlab/mmcv/pull/978, MultiScaleDeformableAttention has been moved to mmcv/ops/multi_scale_deform_attn.py. We have to fix the import path of  MultiScaleDeformableAttention.
BC-breaking
None

https://github.com/open-mmlab/mmdetection/blob/master/docs/get_started.md
This doc should be update that mmdet cannot work with mmcv>=1.3.6 because of this change.


How to get  exact pixel in the Town3 from the world coordinates ?  What is the scaling factor ?

How to get  exact pixel in the Town3 from the world coordinates ?  What is the scaling factor ?

Could you provide more information? pixels of what image are you referring to?

I want to know the pixel values of where actors(pedestrians/vehicles) are drawn in pygame window

Basically you need to build the intrinsic matrix from FOV and resolution of the image (you have an example here to convert depth images to 3D points), then convert to world coordinates using the camera transform.

No sorry, that's to convert from pixel to 3D point, but doing the inverse should be trivial right? :smile:

Oh wait, check out #1383.


Add more sku pack documentation

@RackHD/corecommitters @uppalk1 @keedya @tannoa2

+1

:+1:

looks great, thanks @zyoung51


Please helpme. I got spam notice like this on console

Version: 3.4.3-b508
I got spam notice like this on console, You can check console spam error @ this link https://pastebin.com/DwAar6Mf
My quests.yml > https://pastebin.com/bEJSiCDa
Thanks For Help

Already resolved in recent issues. Use http://ci.ac3-servers.eu/job/Quests/523/
Sent with GitHawk


bugfix: increase load of the peer serving for others

Signed-off-by: lowzj<EMAIL_ADDRESS>
Ⅰ. Describe what this PR did
Ⅱ. Does this pull request fix one issue?

Ⅲ. Why don't you add test cases (unit test/integration test)? (你真的觉得不需要加测试吗？)
Ⅳ. Describe how to verify it
Ⅴ. Special notes for reviews

Codecov Report

Merging #620 into master will decrease coverage by 0.02%.
The diff coverage is 0%.

@@            Coverage Diff             @@
##           master     #620      +/-   ##
==========================================
- Coverage   46.28%   46.26%   -0.03%
==========================================
  Files          99       99
  Lines        5876     5877       +1
==========================================
- Hits         2720     2719       -1
- Misses       2932     2933       +1
- Partials      224      225       +1

Impacted Files
Coverage Δ

supernode/daemon/mgr/progress/progress_util.go
21.42% <0%> (+0.33%)
:arrow_up:

supernode/daemon/mgr/progress/progress_manager.go
26.08% <0%> (ø)
:arrow_up:

supernode/daemon/mgr/scheduler/manager.go
24.61% <0%> (-1.37%)
:arrow_down:

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
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LGTM.


[BUG] Typings Error

Describe the bug
There's a typings error that the TypeScript transpiler shows when transpiling my project. It's something to do with a constraint.
node_modules/eris/index.d.ts:2222:16 - error TS2428: All declarations of 'Message' must have identical type parameters.

2222   export class Message<T extends PossiblyUncachedTextable = TextableChannel> extends Base {
                    ~~~~~~~

node_modules/pluris/index.d.ts:111:13 - error TS2428: All declarations of 'Message' must have identical type parameters.

111   interface Message<T extends Textable = TextableChannel> {
                ~~~~~~~

node_modules/pluris/index.d.ts:112:82 - error TS2344: Type 'T' does not satisfy the constraint 'Textable'.
  Type 'PossiblyUncachedTextable' is not assignable to type 'Textable'.
    Type 'Uncached' is missing the following properties from type 'Textable': lastMessageID, messages, addMessageReaction, createMessage, and 12 more.

112     awaitReactions(options: ReactionCollectorOptions): Promise<ReactionCollector<T>>;
                                                                                ~

Example code to reproduce
// No code is needed.

Expected behavior
There shouldn't be a typings error when transpiling.
Environment

Node version
Eris version
Pluris version

v15.13.0
0.15.1
0.3.5

Additional context

[ ] I know exactly what the issue is and will create a PR to fix it.
[x] I do not know how to fix the bug/issue or I am only reporting the bug and will not create a PR.

Understandable. Should I close this issue now or leave it open until the typings are updated?

npm i bsian03/pluris, full release will be coming later


Components links are incorrect for hyphened Component names

Navigate to an example page like ModalBox
Scroll to a section of the page that displays: "Components used: modal-box"
Click the component link (any component with a hyphen, like modal-box)

current behavior:
An error page displays.
expected behavior:
The component page should display

@jgiardino The search pattern for how we find these classes isn't finding all components, either (for example, ModalBox is listed in the second example, but not the others, even though it's in all of the examples).
Should the list of components include the component you're currently looking at? So in the case of the ModalBox component page, should that component be included in the lists under the examples?
And we currently look for components and layouts for the list in "components used". Should that just look for components? Or separate out "components used: ..." and "layouts used: ..."?

Good catch on the issue that not all components are listed!
I don't have a strong opinion about listing the current component in the list of components. It seems a little weird, but I don't think it would be confusing for anyone if it's impossible to fix that.
I like the idea of listing Layouts along with components. Layouts are an important part of PF4, and if someone is looking at an example, whether it's a component or a demo page, and they're wanting to implement that or create a customized implementation of it, it's helpful to know all the parts that make that work, and to be able to see those parts in isolation.
Related to this feature, at defConf.us, the PatternFly booth had an activity where they asked what information or features are important to consumers when using a design system, and one of the comments had to do with how difficult it is to work with the source code that's provided when the source code sample is everything on a page. I think this feature is a very important for satisfying the use case where consumers want to see examples of full page layouts to see what's possible, but want the source code for the components and layouts that are needed for building it in separate chunks.

Cool thanks! Do you think we leave it as "Components used:" or change it to like "Includes:" or "Components and Layouts used:", or just leave it as is?

"Components and Layouts used:" sounds good to me.