fix(es-setup): create data stream on non-aws

Checklist

[ ] The PR conforms to DataHub's Contributing Guideline (particularly Commit Message Format)
[ ] Links to related issues (if applicable)
[ ] Tests for the changes have been added/updated (if applicable)
[ ] Docs related to the changes have been added/updated (if applicable). If a new feature has been added a Usage Guide has been added for the same.
[ ] For any breaking change/potential downtime/deprecation/big changes an entry has been made in Updating DataHub

This fixes: https://github.com/datahub-project/datahub/issues/6924


Bug updating existing post

I found one more issue. If I update an existing reply, some characters go missing. For every update I do, the characters go missing. Please check one of my previous replies. I'm unable to edit the reply and add the missing characters. If I do so, it again disappears.
I have seen this problem earlier when I try to update an already published post using esteem app. For every update I do, some content from the article will disappear.

can you share screenshot @horpeyloaded

@feruzm  could you please test it with our internal latest build I already fixed but needs to test.  if it's fine we can close this issue.


Typescript in migrations

Should be nice to use Typescript in migrations. https://github.com/bleshik/umzug-typescript-helper#readme

@buresmi7
I was thinking about it and the fact that we have to compile migrations before apply stopped me, I will check the lib and maybe comeup with something, anyway we should support both ts and js migrations.
Thank you.


R200 and odroid N2+ (kernel 4.9.x uvcvideo patch requirement)

Required Info

Camera Model
{ R200  }

Firmware Version
(Open RealSense Viewer --> Click info)

Operating System & Version
{ Ubuntu 18.04)

Kernel Version (Linux Only)
(e.g. 4.9.230-74)

Platform
Odroid N2+

SDK Version
{ legacy   }

Language
{C }

Segment
{Robot }

Issue Description
Hi there,
I'm working on robot that use Odroid N2+ installed ubuntu mate 18.04 and kernel version is 4.9.x.
I have tried to success patch uvcvideo and it can do 'sudo modprobe uvcvideo' just fine. But when i plug in r200 it turn out freeze and reboot itself.
As far as i know, currently Odroid N2+ team have no plan to work with kernel 4.15, 4.18, 5.0 or 5.3.
So i dont have any choice but open issue and looking for help. Any suggestion? If someone have some patch script that can work with kernel 4.9 i will happy to try. Sorry for bad English.
Thank you advanced!

In the modern day RealSense SDK, if there are complications with patching (for example, with a particular computing device) then you can build with the libuvc backend method to avoid the need for kernel patching when building librealsense.  Legacy Librealsense had a similar libuvc backend process to bypass the need for patching.
https://github.com/IntelRealSense/librealsense/issues/73

Hi @fxprime In the modern day RealSense SDK, if there are complications with patching (for example, with a particular computing device) then you can build with the libuvc backend method to avoid the need for kernel patching when building librealsense. Legacy Librealsense had a similar libuvc backend process to bypass the need for patching.
#73

Thank you for replied krub. I will try and share the result soon.

Update :
After i try librealsense from
https://github.com/Maghoumi/librealsense
I have some error as show below
src/image.cpp:12:14: fatal error: tmmintrin.h: No such file or directory
     #include <tmmintrin.h> // For SSE3 intrinsics used in unpack_yuy2_sse

Am i missing something? By the way, i try to force cmakelist (of librealsense version 1.12.4 ) to use libuvc by set
set(BACKEND RS_USE_LIBUVC_BACKEND)

After that, i build librealsense 1.12.4 and try to run command
cpp-tutorial-1-depth

It's seem work properly. But when i try to run
cpp-config-ui

and press "Start capture" The error show up as below

And terminal error show as below
odroid@odroid:~$ cpp-config-ui
Capturing DEPTH at 628 x 468, fov = 56.6 x 43.8, distortion = NONE
Capturing COLOR at 640 x 480, fov = 54.0 x 41.5, distortion = MODIFIED_BROWN_CONRADY
Capturing INFRARED at 640 x 480, fov = 57.5 x 44.8, distortion = NONE
Capturing INFRARED2 at 640 x 480, fov = 57.5 x 44.8, distortion = NONE
RealSense error calling rs_start_source(device:0x557310ee40, source:VIDEO):
    uvc_set_ctrl(...) returned LIBUSB_ERROR_PIPE
X Error of failed request:  GLXBadDrawable
  Major opcode of failed request:  154 (GLX)
  Minor opcode of failed request:  11 (X_GLXSwapBuffers)
  Serial number of failed request:  15800
  Current serial number in output stream:  15800

Any suggestion?
Thank you advanced krub:)

Considering that the newest parts of the Maghoumi fork of Legacy Librealsense are 4 years old, it may be too out of date to work now.
It is a good sign that the cpp-tutorial-1-depth program was able to work for you after building the official Legacy Librealsense and using the libuvc backend method.
For the information of others reading this, cpp-config-ui is a program for Legacy Librealsense that provides a visual configuration interface like the one that the RealSense Viewer provides in librealsense2.
https://idorobotics.com/2020/02/19/optimising-the-realsense-r200-camera-for-outdoor-use/
Some information on the error that you experienced with it can be found in the link below:
https://github.com/IntelRealSense/librealsense/issues/72

Considering that the newest parts of the Maghoumi fork of Legacy Librealsense are 4 years old, it may be too out of date to work now.
It is a good sign that the cpp-tutorial-1-depth program was able to work for you after building the official Legacy Librealsense and using the libuvc backend method.
For the information of others reading this, cpp-config-ui is a program for Legacy Librealsense that provides a visual configuration interface like the one that the RealSense Viewer provides in librealsense2.
https://idorobotics.com/2020/02/19/optimising-the-realsense-r200-camera-for-outdoor-use/
Some information on the error that you experienced can be found in the link below:
#72

I have tried to update r200 firmware to <IP_ADDRESS> (Is it lastest version ?)  using FWUpdater as you suggestion.
It worked !  But only when i enable auto LR. When i disable auto LR it turn out as below. I think my version is still not up-to-date i guess?

https://github.com/IntelRealSense/librealsense/issues/73 suggests that the libuvc backend installation method was more experimental than it is in the present day with librealsense2, and that occasional unplug / replug of the camera to reset it may be necessary.

#73 suggests that the libuvc backend installation method was more experimental in the era of librealsense1 than it is in the present day with librealsense2, and that occasional unplug / replug of the camera to reset it may be necessary.

Thank you krub. This can close for now.

Thanks so much for the update!


Use clean window period variables

Currently, when we calculate window period we do
https://github.com/influxdata/influxdb/blob/a2071478c804fdbaf8dbefdbdc013f63bd326973/ui/src/variables/utils/getWindowVars.ts#L66-L68
The result is that we end up sending a lot more data to the client than what we would expect.
We should have pre-defined bucket sizes like 10s, 30s, 1m, 5m, 10m, 30m, 60m, etc and round to the nearest depending on the duration of the query.

currently the duration to window period relationship is

duration
window period

5m
0.83s

15m
2.5s

1h
10s

6h
60s

12h
120s

24h
240s

2d
480s

...

what would be a more appropriate relationship do you think?

waiting for https://github.com/influxdata/influxdb/pull/16385/files


Context manager

This PR introduces a context manager to update repositories on the hub. It is using the Repository backbone.
:warning: I am not sure of the Commit name, but I can't think of anything better. If you have ideas please mention them!
The proposed implementation handles repo_id, user/repo_id and org/repo_id as model identifiers. It locally syncs the repository with the online repository on the hub and manages it in a very git-like fashion:

It clones the repository if it isn't here locally
It pulls the recent changes with rebase if the repo is already here
It adds changes, commits, and pushes.

Example usage:
With text files
with Repository("text-files", clone_from="Jikiwa/text-files", use_auth_token=True).commit("My first file :)"):
    with open("file.txt", "w+") as f:
        f.write(json.dumps({"hey": 8}))

# Repo is created and available here: https://huggingface.co/Jikiwa/text-files

With PyTorch
import torch

model = torch.nn.Transformer()

with Repository("torch-model", clone_from="Jikiwa/torch-model", use_auth_token=True).commit("My cool model :)"):
    torch.save(model.state_dict(), "model.pt")

# Repo is created and available here: https://huggingface.co/Jikiwa/torch-model

With Flax
from flax import serialization
from jax import random
from flax import linen as nn

model = nn.Dense(features=5)

key1, key2 = random.split(random.PRNGKey(0))
x = random.normal(key1, (10,))
params = model.init(key2, x)

bytes_output = serialization.to_bytes(params)

with Repository("flax-model", clone_from="Jikiwa/flax-model", use_auth_token=True).commit("My cool Flax model :)"):
    with open("flax_model.msgpack", "wb") as f:
        f.write(bytes_output)

# Repo is created and available here: https://huggingface.co/Jikiwa/flax-model

Play with it in colab:
https://colab.research.google.com/drive/1jq-FaTpC4aJFHMaZVuVhT1id09AD90Ga?usp=sharing
Remaining TODOs
Once we align on the API, will need to do the following:

Test the datasets & spaces repositories
Write some docs

maybe with Repository("Jikiwa/flax-model").commit(commit_message="commit")?
or with Repository.commit("repo_id", commit_message="commit")?
not sure what's best 🤷

Re-did the approach with Repository(xxx).commit("Msg"). Cleaner this way, thanks for the tip!

Merging with offline approval from @julien-c


Deprecation Warning

This library is outputting a deprecation warning and very little documentation in a path to move forward.
/home/fred/.local/lib/python3.9/site-packages/okta_jwt_verifier/jwt_verifier.py:39: DeprecationWarning: JWTVerifier will be deprecated soon. For token verification use IDTokenVerifier or AccessTokenVerifier. For different jwt utils use JWTUtils.

@azafred, @abhaynayak24 Version 0.2.2 has been released https://github.com/okta/okta-jwt-verifier-python/releases/tag/v0.2.2
I'm closing this issue as resolved. Feel free to reopen this issue or create a new one if your problem still exists.


Change Mint Bills icon

The Mint Bills logo is actually different from Mint.com :/

Seems like the link check timeouted?

Yeah don't worry about it. It happens to everyone

i originally didn't want to use 2 different logos since they are so similar, it's kind of irrelevant. ;)


Update

CI fails, I am not sure what the cause is. I had the same error locally, but after removing upstream/opentest4j.git and re-executing clone, I did not get the error any more

CI was fixed by rebuilding container image.


Merge Faster

12 hours still seems slow, lets go even faster! Benefits include being able to patch bugs much quicker, rather than the bot being half dead while waiting on a patch.

If it gets too low it'll be easy to troll or through in features without a proper review.
I would settle for eight hours and a raise in the quorum requirement from 2 to 4.

Actually six hours is fine as long as the quorum number is adjusted up.

Would it be possible to have quorum dynamically switch timeouts depending on LoC change?
It seems like 6 hours, or even 12 hours might be a bit short for a large paradigm shift of the project - while we may want even 3-4 hours for simple bug fixes.
I'll have to look and see if this is possible to add to gitconsensus, but would anyone be in support?

@daboross - that's something that would have to be added to gitconsensus.

Would you be against it being added?

Oops, apparently I missed you last line. Yeah I think it would be interesting to add and would totally support that pull request upstream.

This Pull Request has been merged by GitConsensus.
Vote Totals

Yes
No
Abstain
Voters

4
0
0
4

Vote Breakdown

User
Yes
No
Abstain

ags131
✔

lurr
✔

tedivm
✔

anisoptera
✔

Vote Results

Criteria
Result

Has Quorum
True

Has Votes
True


Traceback (most recent call last):   File "train.py", line 434, in

Hello, I have try to run your code by your sample cmd
python train.py --phase train or test --dataset_path .../mvtec_anomaly_detection --category carpet --project_root_path path/to/save/results --coreset_sampling_ratio 0.01 --n_neighbors 9

You can try to install torch==1.7.1


Failed VR health check gateways_check.py on additional public IP range

Steps to reproduce the issue

Use multiple IP-Ranges / VLANs on Public Physical Network
Create VPC
Create multiple tiers
Deploy one VM in every tier
Allocate IP-Adresses from different ranges / vlans
Create Static NAT for at least one VM with an IP not originating from snat-range.
Execute health-checks

More information

In the VPC VR, there are two public nics

eth1 - xxx.xx.179.15/24
eth2 - xxx.xx.178.11/24
The health check gateways_check.py failed with error message
Unreachable gateways found - xxx.xx.178.1

switch says "Rule Interface Mismatch"

and drops these packets

The iptables rules shows

-A POSTROUTING -j SNAT -o eth1 --to-source <IP_ADDRESS>
-A POSTROUTING -j SNAT -o eth2 --to-source <IP_ADDRESS>

the issue is fixed by changing the rules to
-A POSTROUTING -o eth1 -j SNAT --to-source <IP_ADDRESS>
-A POSTROUTING -o eth2 -j SNAT --to-source <IP_ADDRESS>

ISSUE TYPE

Bug Report

COMPONENT NAME

VR

CLOUDSTACK VERSION

<IP_ADDRESS>

CONFIGURATION

OS / ENVIRONMENT

SUMMARY

STEPS TO REPRODUCE

EXPECTED RESULTS

ACTUAL RESULTS

this issue seems to be caused by https://github.com/apache/cloudstack/pull/8599
there is no other pf/lb/dnat issues found until now


How to adjust  character height in  touchpadwalking scene?

I need my camera around like a normal person contrast objects.The parameter is greater than 0.8 will penetrate the ground.thanks.

The parameter  is the Headset Y offset.


Introduce PCA in results

We need to perform Principal Component Analysis for :

isolating exceptionnal results that disturb the y axis scale
highlight results in a more meaningful way than mean, Standard Deviation and Standard Error of the Mean

No need for such complex analysis as we now have a scatter point chart


CASMHMS-632 Add HSM component endpoint unit tests

Summary and Scope
This change adds the following HSM component endpoint unit tests:

TestDoComponentEndpointGet(), 4 test cases
TestDoComponentEndpointsGet(), 8 test cases
TestDoComponentEndpointDelete(), 5 test cases
TestDoComponentEndpointsDeleteAll(), 5 test cases

Issues and Related PRs

Resolves CASMHMS-632.

Testing
This change was tested by executing the new HSM unit tests locally as well as in the Jenkins build pipeline and verifying that they passed.
Was a fresh Install tested? N
Was an Upgrade tested? N
Was a Downgrade tested? N
Risks and Mitigations
Low risk.

I would argue that no change to this repo is needed for a unit test only change

This isnt needed as this will not impact deployed artifact; so we wont update charts.


Restores behavior of wikidata buttons

The border-box on the other buttons was unintentionally inherited to these. Set these explicitly to content-box instead.

Do you have a screenshot of the issue?

Deployed!

Attach it for archival reasons, this is how it looked before.


Add Benchmarks & Split/Unroll-scopes optimizations (x1.6 faster)

For issue #158:

benchmark branch: Added a benchmark with meta_schemas cheking & extensive sample-schema with references.
Implemented two optimizations:
2.a. split_scopes branch: Keep URIs in 2 parts (url, fragment) to avoid fragging/defragging since fragments do not join, they always override (~1.1 faster)
2.b. unroll_scopes branch:  Replace resolver.in_scope() context-manager with a push/pop stack of scopes, that does nothing it when no id property exists (~1.5 faster).
split_unroll_scopes branch: Combined both optimizations (~1.6 faster).
For future reference, the timed-results are engraved on the respective methods of wltp/test/test_benchmarks.py TC (unfortunately they are host-specific).

ALL TCs RUN AS OK (apart from split_frags which was post-edited on the combined branch)

This changes improve the performance on my benchmark by nearly 2x.
What can I do to help get this merged?  It looks like the tests are failing and it needs to be rebased with msater.

@Julian Would it be possible for you to take a look at this branch? Our project has a relatively simple schema, and validation using jsonschema is frequently the slowest part of our code. A performance increase of 2x would be a significant benefit to us. As @dnephin mentioned, what can I do to help get this merged in?

Hi @cgurnik -- @dnephin already successfully managed to get in some performance changes -- can you give your project a shot on current master and see if you get any difference?
I'm trying to find some time to do a release but I was overseas for a few days.
If you don't see results can you possibly include some profiling output, would love to have a look!

Most of the changes from this branch were included in my branch, so it might even be appropriate to close this one.

@dnephin Do you mean that this dnephin@1c15827713cbcf43c4fac8cfa9e5c3e50a501e96 (branch: perf_take2) contains roughly the 2 perf-improvements, but not #184?

Right, I included many of them in https://github.com/dnephin/jsonschema/commit/2fda1556ef7714d562317049f46db6ff59db5c2f


Write an RFC for an EVM backend for Sway

In-depth description to follow.

I'm closing this issue as the RFC has been most written.


Adding containerd-node-throughput job config

Adding node-throughput prow job config
Adding namespace metadata to perfdash deployment template.

/cc @mkumatag
/assign @piyushgupta1551

/cc @Rajalakshmi-Girish

Closing this PR. Will raise 2 new ones with review comments as I need to delete my fork and redo the changes due to branch conflicts at my end.


Breakout new context/callback funcs

Signed-off-by: Eric Windisch<EMAIL_ADDRESS>

Fixes #77 #75

Coverage decreased (-0.3%) to 89.231% when pulling 5eaec778443314cce22b95972070bc370b6bbcb1 on ewindisch:refactor_newfuncs into 265872b73756fbf5bf788e4bbed2867ad6656be8 on iopipe:master.

Coverage decreased (-0.3%) to 89.231% when pulling cfa74a9c7c19689ab3d14c820e9f3085b4490423 on ewindisch:refactor_newfuncs into 265872b73756fbf5bf788e4bbed2867ad6656be8 on iopipe:master.


Bug after using Message Box Functions Versions are 0.9.53

After using the locateOnScreen、locateAllOnScreen、locateCenterOnScreen functions, the calls to the keyboard functions have no effect, but the calls to the mouse functions are normal.This could be a BUG,or maybe it's a problem with my system.If it's a bug please fix it,Thank you very much!Best wishes to everyone!
On my operating system:ubuntu 20.04,python interpreter:3.8.10,and scrot、python3-tk、python3-dev、PyTweening、PyScreeeze、PyGetWindow、PymsgBox、MouseInfo are all installed.

Sorry, I wrote the wrong function name, it should be pyautogui.alert(), pyautogui.confirm(), pyautogui.prompt() these functions

I'm sorry, this is a cheap mistake I made, the reason the keyboard function fails after calling 'Message Box Functions' is that the window I want to type in has no focus.


GPU is not used in python wheel generated for GPU

Please make sure that this is a build/installation issue and also refer to the troubleshooting documentation before raising any issues.
System information (Please provide as much relevant information as possible)

OS Platform and Distribution (e.g. Linux Ubuntu 16.04, Android 11, iOS 14.4): Ubuntu 18.04, Ubuntu 20.04
Compiler version (e.g. gcc/g++ 8 /Apple clang version 12.0.0):gcc/g++ 8
Programming Language and version ( e.g. C++ 14, Python 3.6, Java ): Python
Installed using virtualenv? pip? Conda? (if python): venv
MediaPipe version: v0.8.9
Bazel version:
XCode and Tulsi versions (if iOS):
Android SDK and NDK versions (if android):
Android AAR ( if android):
OpenCV version (if running on desktop): <IP_ADDRESS>

Describe the problem:
I'm experiencing an unexpected behavior after successfully generating a .whl of mediapipe with the changes required to use the GPU according either to the solution proposed in issues #1651 or #3266. The build process is successful and I can pip install the .whl and run facemesh in python. The issue is that the CPU usage is unchanged (around 350% on a coupleryzen7 4000 series) compared to the CPU version and from GPU monitoring tools such as corectrl and nvidia-smi the GPU looks to be unused by the process. This behavior was encountered both on AMD RADEON and on NVIDIA GPU, both on Ubuntu 18.04 with python 3.6 and 3.8 and on Ubuntu 20.04 with python 3.8.
From the logs it looks like it is running the GPU graphs but this is inconsistent with the CPU and GPU activity monitored.
Provide the exact sequence of commands / steps that you executed before running into the problem:
After modding the code accordingly to #1651 or after installing mediapipe from the repo linked in #3266 I do the following steps:

I create a venv in the parent directory of mediapipe so that I have /parent/mediapipe and /parent/venv
python3 -m venv venv && source venv/bin/activate
I upgrade pip, install wheel and setuptools
pip install -U pip
pip install wheel setuptools
I generate the protos and the wheel
python setup.py gen_protos && python setup.py bdist_wheel
4)I install the wheel in the venv
pip install mediapipe-dev-cp38-cp38-linux_x86_64.wheel
5)I successfully launch the basic mediapipe facemesh script as shown in the docs.

Complete Logs:
Include Complete Log information or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached:
This is the output of the bash shell after I launch a basic facemesh  script:
WARNING: Logging before InitGoogleLogging() is written to STDERR
I20220517 16:02:06.958925 93230 gl_context_egl.cc:163] Successfully initialized EGL. Major : 1 Minor: 5
I20220517 16:02:06.969666 93260 gl_context.cc:331] GL version: 3.2 (OpenGL ES 3.2 Mesa 21.2.6)
I20220517 16:02:06.969858 93230 gl_context_egl.cc:163] Successfully initialized EGL. Major : 1 Minor: 5
I20220517 16:02:06.971607 93262 gl_context.cc:331] GL version: 3.2 (OpenGL ES 3.2 Mesa 21.2.6)
INFO: Created TensorFlow Lite delegate for GPU.
INFO: Replacing 164 node(s) with delegate (TfLiteGpuDelegate) node, yielding 1 partitions.
INFO: Replacing 97 node(s) with delegate (TfLiteGpuDelegate) node, yielding 1 partitions.

Hi @mtrtn2u ,
MediaPipe python solutions supports only for CPU graphs ,You can follow https://github.com/google/mediapipe/issues/1651#issuecomment-790176010 to run the GPU graphs. Sorry for the inconvenience.

Hi @sureshdagooglecom. That is exactly what I did to run the GPU graphs, maybe I've not been very clear. The issue is that the  procedure described in #1651 leads me to a wheel that does not use any GPU resources.

Is there anyone that can help with my issue? I've tried to generate again the wheel following once again #1651 but to no avail. @sureshdagooglecom  could you look into it?

Hi @mtrtn2u,
we actually had exactly the same problem in my team: we could successfully run mediapipe (actually, the face mesh solution) with the GPU features, but only using the original (c++) code. We tried as well to generate a python wheel, but apparently the software always went on the cpu only, no matter the modifications we applied on the code/compilation files. I really would appreciate if this issue could be solved, since we ended up with using mediapipe as a stand-alone tool...

UPDATE:
I went back as far as to version v0.8.3, with modifications to the graphs, BUILD and setup.py done accordingly to the calculators and stuff that version contains. The results are once again the same. CPU usage is always over 360% on ryzen7 series 4000. GPU usage both on Nvidia and AMD is none. I assume that those modifications , as in #1651,  only lead to a successful  build process that doesn't actually reflect on actual usage of GPU resources. If true hat would be a shame as the python wrapper is very much practical and straightforward to use.

Hi @mtrtn2u, we actually had exactly the same problem in my team: we could successfully run mediapipe (actually, the face mesh solution) with the GPU features, but only using the original (c++) code. We tried as well to generate a python wheel, but apparently the software always went on the cpu only, no matter the modifications we applied on the code/compilation files. I really would appreciate if this issue could be solved, since we ended up with using mediapipe as a stand-alone tool...

Hello @sergionocco,
Thanks for your comment.

So, if I use the mediapipe face mesh original c++ code, I will be able to run it on GPU. is that right?
Did running on GPU increased the speed/FPS/performance?
I appreciate if you share you experience.

Hi @lghasemzadeh,

I think so, we properly modified the compilation files in order to run the GPU graphs, and yes, apparently with the original c++ code the GPU was indeed used (though the gpu usage percentage was anyhow quite small)
Yes, but not in the sense that you mean. The fact is that we had to process in real time a video stream with a low frame rate (about 10 Hz), but the full processing pipeline (acquisition + mediapipe + subsequent algorithms) had to run on an embedded system with small resources, something like a jetson nano. So, for us, it was important to save the processor usage by demanding what was possible to the gpu... and yes, doing it this way, the full on-board processing was possible.

Thanks for the reply @sergionocco .
I am in same situation as you are :) I need to run my code efficiently on jetson nano.
we I run my algorithms locally on my desktop, I get this notification in terminal (INFO: Created TensorFlow Lite XNNPACK delegate for CPU) also I check the GPU and CPU usage and it seems it uses CPU most, the FPS is around 30.
But when I run the same codes on nano, without any modification in mediapipe instruction, I get the notification of using GPU! (INFO: Created TensorFlow Lite XNNPACK delegate for GPU) but when I check the usage it seems it is using CPU, not GPU. and the FPS for the same code is 6. FPS decreases dramatically and I get an undesired latency in my algorithm.
My questions are:

Without using c++, can I run my python codes on nano using GPU?
If I am not mistaken you mentioned that you succeed to run python codes on GPU in the above comment. How you did that? would you please share?
have you tried the solution in this issue #3266?
Thanks

Well @lghasemzadeh,
first of all, in my experience, running mediapipe with GPU support simply reduces the CPU usage, but, apparently, the big job is still performed by the main processor. To give you an idea, on my desktop pc, running mediapipe without the GPU gives a CPU usage around 130%, and it decreases to about 100% when the GPU is also enabled. Then:

ehm, I don't know, I guess this is the object of the issue opened by @mtrtn2u. We did run python codes on jetson nano, but subsequently to mediapipe, which indeed was executed as a stand-alone process using the original c++ code.
actually no, I said that we struggled to incorporate the mediapipe python byndings (with GPU support) in our scripts, but in the end we gave up, and finally ran mediapipe as a separate process.
well, not really, I will have a look, thank you for the link...

I hope this comment will be useful to you and to any other interested in this matter...

Hi @sergionocco and @lghasemzadeh. I have the feeling we are experiencing all the same issue. I want to be able to effectively run the gpu  python mediapipe version on jetson nano. Meanwhile I did find out that there was a collateral issue that bumped up my CPU consumption that high, but even after correcting that mediapipe in python still consumes an awful lot of CPU resources, indeed too much for a jetson nano.
As you may read in my original issue I too get the notification that mediapipe is using GPU (INFO: Created TensorFlow Lite XNNPACK delegate for GPU) but that doesn't reflect on actual GPU usage.
On the other hand implementing everything I need to do into a custom mediapipe solution in c++  is not practical at all thanks to Bazel that is cryptic to work with, to say the least.

Actually I don't mind if CPU or GPU is under load. I only want to increase my algorithm FPS.
For my custom python algorithm which uses mediapipe, on jetson nano, I get FPS of 6, and for another mediapipe-based algorithm I get FPS of 2, they are really low and they are not suitable for real-time applications.
 So I changed or deleted some of calculations in the code, and FPS increased. But this is not the correct way to aaproach this problem.
so I decided to use GPU to reduce the CPU usage which cause increasing FPS.

@sergionocco how you enabled GPU on desktop PC? I even want that slightly reduction of CPU usage.
@mtrtn2u what was the collateral issue you found? How you fixed it?

@bossebandowski what do you think?

@lghasemzadeh  Unfortunately for me, I'd rather have mediapipe running on GPU because I need the CPU to be available for other hefty processes and as you mentioned 2 or even 6 FPS are definitely not enough. The collateral issue I had was with code that takes in input the face_landmarks from facemesh to compute geometrical features, so it is not really related to mediapipe per se.
I close the issue as there seems to be no solution for it.

@lghasemzadeh
Looks like this is getting a bit messy. Here's where we're at:

We have not yet managed to run the python wrapper with mediapipe on any platform using the GPU.
We are currently not looking at embedded systems but have done so in the past. This repo provides an installation guide for aarch64 architectures and we managed to get working builds, but again without using the GPU
In general, there is no problem with using the GPU from python. Have you looked at other pose estimation frameworks? We played around with trt_pose which gets the job done at a much higher framerate, but the model performance seems to be worse.

@mtrtn2u
Hi, I have encountered very similar issues, and somehow managed to figure it out.
I'm using ubuntu 22.04 with a Nvidia rtx3070 card, and I want to run mediapipe holistic model on GPU using python interface.
I modified those files (holistic landmark pbtxt, setup.py, BUILD, etc) as #1651 (comment) did, but on the newest mediapipe-v0.8.11, then successfully built a .whl, and pip installed it.
Executing it gives very similar logs like yours:
WARNING: Logging before InitGoogleLogging() is written to STDERR
I20221207 10:57:13.676404 77499 calculator_graph.cc:123] Initialize the GPU.
I20221207 10:57:13.694615 77499 gl_context_egl.cc:84] Successfully initialized EGL. Major : 1 Minor: 5
I20221207 10:57:13.705412 77575 gl_context.cc:335] GL version: 3.2 (OpenGL ES 3.2 Mesa 22.0.5)
I20221207 10:57:13.705475 77499 calculator_graph.cc:127] Succeeded get GPU
INFO: Created TensorFlow Lite delegate for GPU.

In this case, mediapipe holistic on this "GPU" version runs much slower than on CPU version (~70 ms on "GPU" vs ~30 ms on CPU), and of course nvidia-smi confirms that no gpu is using.
Then I found this comment, the replier succesfully runs mediapipe using python interface on GPU. You can see that the OpenGL runs on Nvidia libs, but not Mesa libs:
I20220523 19:14:59.332114 556636 gl_context.cc:334] GL version: 3.2 (OpenGL ES 3.2 NVIDIA 470.129.06)

You can also check this by using glxinfo (the official doc also mentioned it, and you can see that Nvidia libs are used.)
I'm not familiar with these GL stuffs, but I found out that my ubuntu use intel graphic card as default. and I switched it to nvidia graphic card using prime-select:
sudo apt-get install nvidia-prime
prime-select nvidia
reboot

and it works in my environment. The log shows that OpenGL runs on Nvidia libs now:
WARNING: Logging before InitGoogleLogging() is written to STDERR
I20221208 15:04:13.921077 113265 gl_context_egl.cc:86] Successfully initialized EGL. Major : 1 Minor: 5
I20221208 15:04:13.957674 113332 gl_context.cc:335] GL version: 3.2 (OpenGL ES 3.2 NVIDIA 515.48.07)
I20221208 15:04:13.957754 113265 gl_context_egl.cc:86] Successfully initialized EGL. Major : 1 Minor: 5
I20221208 15:04:13.969427 113333 gl_context.cc:335] GL version: 3.2 (OpenGL ES 3.2 NVIDIA 515.48.07)
INFO: Created TensorFlow Lite delegate for GPU.

Mediapipe holistic python interface now runs 15ms per frame on my machine (compared to 30ms per frame on CPU).

@siyuzou thanks for your insight. I have since moved on from this topic given the not so good results I was getting. Your workaround looks worth one more attempt, I will report back once I manage to try it.

@siyuzou I since put aside this topic given the results I was getting were not what I expected but your workaround look worth one more attempt. I will try it out and report back if it works. Thanks for your insight.

@siyuzou would you be able to share your modifications for v0.8.11? I am trying to reproduce the very same setup. Thanks in advance!


ExchangeContext: Implement a safer verion Abort/Close

Problem
Fixes #19747 Use-after-free when aborting already-closed exchange
Change overview

Introduce safer version Abort and Close for ExchangeContext

Abort or Close can be called at anytime on a valid exchange which is not releasing.

Once Abort or Close is called, or OnExchangeClosing is triggered, the exchange is releasing

The application should lose its control to that exchange. It must null-out all pointers to that exchange
The exchange will be released sooner or later.

This change is based on following concepts

Exchange refcounts are managed by internal states rather than by applications.
Instead of manually manipulate refcounts, Applications manage Exchange states.

Only have 2 states now, Closed or not, identified by kFlagClosed.
The initial refcount is bound to "not closed state" (kFlagClosed is not set)

Note: Currently, there are 3 types of ref counts to an exchange

The initial value: controlled by kFlagClosed
The scoped ExchangeHandle in member functions of ExchangeContex
The ExchangeHandle in RetransTableEntry

Testing
Verified by unit-tests.

Verified by unit-tests.

And fwiw, I don't see any tests in this PR covering the interesting scenarios here, and I am quite sure our existing unit tests do not exrcise them.

I'd tend to agree as well that this is likely something we want post 1.0.


API to work with naked Dictionary_

For tight inner loops a double indirection of Dictionary (pointer to MutVar# + pointer to Dictionary_) might have a significant performance cost. I'd like to have something like
insert_ :: (MVector ks k, MVector vs v, PrimMonad m, Hashable k, Eq k)
        => Dictionary_ (PrimState m) ks k vs v -> k -> v -> m Bool

If Dictionary_ is sufficient to insert without resize, return True. Otherwise return False, so that a user can wrap their Dictionary_ into DRef and MutVar, pass to a usual insert, which will perform all necessary rituals to grow sizes, and extract naked Dictionary_ back.
(A specialized API to resize Dictionary_ explicitly would be even better, but that's a bonus).

Sure, it can be done.
We should probably try to replace the record with a mutable array like in the `structs' package.


Task execution template

Please ask questions about why and how I did things!
This package includes a generic TaskExecutor interface. A TaskExecuter can

Execute an arbitrary task function.
Check the status of a task.

r @yoninachmany @rachelhongca

@marcosnils Thanks again for all these comments, they were very helpful. You don't have to continue commenting if you are busy (I am happy to answer Yoni's questions).

@sandlerben np, glad to help. I'll unsubscribe from this thread so mention me if I can help with anything else.

@yoninachmany look good?

lgtm good job!


Add ServiceImport CRD

ServiceImport CRD will be installed during deploy broker and join.
Signed-off-by: Aswin Surayanarayanan<EMAIL_ADDRESS>

Hi @aswinsuryan ,
does it mean that I will not have to explicitly create ServiceExport object for Service-Discovery multicluster ?

Hi @aswinsuryan ,
does it mean that I will not have to explicitly create ServiceExport object for Service-Discovery supercluster ?

That does not change with this, It is just to create the definition for ServiceImport, the object.

Closing, this was already merged in another PR as result this PR has now 0 changed files
https://github.com/submariner-io/submariner-operator/blob/master/deploy/lighthouse/crds/serviceimport_crd.yaml


[google-cloud-cpp] update to the latest release (v2.21.0)

Updates google-cloud-cpp to the latest release (v2.21.0).

Refactor to reduce duplication. Also install mock libraries for features that have them. e.g.
google-cloud-cpp provides CMake targets:

  find_package(google_cloud_cpp_dialogflow_es_mocks CONFIG REQUIRED)
  target_link_libraries(main PRIVATE google-cloud-cpp::dialogflow_es_mocks)

google-cloud-cpp provides pkg-config modules:

    # Mocks for the Dialogflow API C++ Client Library
    google_cloud_cpp_dialogflow_es_mocks

Tested locally (on x64-linux) with:
for feature in "servicehealth" "dialogflow-cx" "dialogflow-es"; do ./vcpkg remove google-cloud-cpp; ./vcpkg install "google-cloud-cpp[core,${feature}]" || break; done

and
./vcpkg remove google-cloud-cpp && ./vcpkg install 'google-cloud-cpp[*]'

[x] Changes comply with the maintainer guide.
[x] SHA512s are updated for each updated download.
[x] The "supports" clause reflects platforms that may be fixed by this new version.
[x] Any fixed CI baseline entries are removed from that file.
[x] Any patches that are no longer applied are deleted from the port's directory.
[x] The version database is fixed by rerunning ./vcpkg x-add-version --all and committing the result.
[x] Only one version is added to each modified port's versions file.

@dbolduc Could you please solve the conflicts? Thank you!

@dbolduc Could you please solve the conflicts? Thank you!

Fixed. Sorry about that.


How to downgrade Spyder after conda install anaconda?

I've ran conda install anaconda in the base environment, so now every virtual environment has Spyder 4.1.4, but there are a few bugs I wish to confirm are due to 4.1.4 and not something else, for which I need 4.1.3. Above is the only command I ran in base after a fresh Anaconda install - then:
conda create --name env --clone base
conda activate env
conda uninstall spyder
conda install spyder==4.1.3

This fails, and generates a long list of package conflicts (which is strange; why would "batteries included" packages conflict so heavily with themselves). 4.1.4 also seems to have new conflicts with conda install keras==2.3.1, and spyder-unittest was harder to install. This also fails, and I further tried conda update --all and conda clean --all.
Is there a way to downgrade to 4.1.3 or should I reinstall Anaconda without running conda install anaconda in base?

No straightforward way to do it, as suggested by a maintainer's comment. Install in a fresh env, or uninstall enough packages until conda allows you to install 4.1.3 (not easy).


add campus sides to filters

only issue with this is if some rooms open up in buildings that are already full, they won't have a campus location assigned to them

Everything looks good. Approved. Thanks for your contribution!


Azure.Identity Support for Managed Identity for Azure Arc Applications

Managed identities for Azure resources provide Azure services with an automatically managed identity in Azure Active Directory. Using a managed identity, you can authenticate to any service that supports Azure AD authentication without having credentials in your code.
https://azure.microsoft.com/en-us/services/azure-arc/
https://docs.microsoft.com/en-us/azure/active-directory/managed-identities-azure-resources/services-support-managed-identities#azure-arc-enabled-kubernetes

This is ready to start testing!
Simple instructions:

Create a non-Azure VM.
Install azure arc.
Make sure your user has admin privileges and has the correct group.
Run your test.

Ask internally for more details.


Hide Notifications if app is in foreground (android and ios)

How can I achieve notifications are not displayed if app is in the foreground on android/ios?
Only the event OnNotificationReceived or another event should be triggered.

Just add the silent key to the data payload .
    "data":
    {
        "title": "Testtitel",
        "body" : "Notification Hub test notification2",
        "silent" : "true"
    }


Typo's need to be fixed

https://github.com/Project-Owl/dms-lite-docker/blob/main/docker-compose-wifi.yml
serialwriter:
Needs to be renamed so it doesn't trick the user that they are using the wrong image.
READ.MD needs to be updated with this new line:

If you would like to see the logging output of all the containers running then run docker-compose -f docker-compose-serial.yml or docker-compose-wifi.yml up. This will show a bunch of output of the running containers. If you would like to close out the service hit ctrl+c on your machine's keyboard.

These both have been updated :)


Can WhereHows support ms sql server datasets?

Can WhereHows support ms sql server datasets? Or any plan to support it?

Dear issue owner,
Thanks for your interest in WhereHows. We have recently announced DataHub which is the rebranding of WhereHows. LinkedIn improved the architecture of WhereHows and rebranded WhereHows into DataHub and replaced its metadata infrastructure in this direction. DataHub is a more advanced and improved metadata management product compared to WhereHows.
Unfortunately, we have to stop supporting WhereHows to better focus on DataHub and offer more help to DataHub users. Therefore, we will drop all issues related to WhereHows and will not accept any contribution for it. Active development for DataHub has already started on datahub branch and will continue to live in there until it's finally merged to master and project is renamed to DataHub.
Please check the datahub branch to get familar with DataHub.
Best,
DataHub team


Decide on an initial design

I'd like to add @GawainLynch and @erinbtaylor to this discussion as well.
I can make an initial draft and publish it on zeplin or something. If we get an actual designer to join that would be great, but I'll probably manage prototyping.
Things I'd like to push for:

Component modularity
Allowing us to move stuff around until we find something that works for us.

Integrating social media on the page
As much of the conversation seem to happen on twitter, it makes sense to me to integrate it into the webpage. Some options are lists, hashtags or just the official account.

Content spotlight
A section for content spotlight where we can highlight things that are important or current, for instance the Berlin Report or any upcoming event.

Moving the discussion to #20 as I must have missed it :)


iris_monitors.py broken in release r1.0 due to inacurate MetricSpec namespace

NOTE: Only file GitHub issues for bugs and feature requests.  All other topics will be closed.
For general support from the community, see StackOverflow.
To make bugs and feature requests more easy to find and organize, we close issues that are deemed
out of scope for GitHub Issues and point people to StackOverflow.
For bugs or installation issues, please provide the following information.
The more information you provide, the more easily we will be able to offer
help and advice.
What related GitHub issues or StackOverflow threads have you found by searching the web for your problem?
tf.contrib.learn.metric_spec.MetricSpec should be changed to MetricSpec in iris_monitors.py in release 1.0.0
No need to import MetricSpec in iris_monitors.py it's already imported on line 24 in commit fa4ba830f437fdb9dc1085b4d68a3bab41a16e20:
tensorflow.contrib.learn.python.learn.metric_spec import MetricSpec
Environment info
Operating System:

tensorflow/tensorflow/examples/tutorials/monitors$ lsb_release -a
No LSB modules are available.
Distributor ID:	Ubuntu
Description:	Ubuntu 16.04.1 LTS
Release:	16.04
Codename:	xenial
tensorflow/tensorflow/examples/tutorials/monitors$ uname -a
Linux panchito 4.4.0-62-generic-tuxonice #83~ppa1-Ubuntu SMP Thu Feb 2 23:17:45 UTC 2017 x86_64 x86_64 x86_64 GNU/Linux

Installed version of CUDA and cuDNN:
(please attach the output of ls -l /path/to/cuda/lib/libcud*):
If installed from binary pip package, provide:

A link to the pip package you installed:
The output from python -c "import tensorflow; print(tensorflow.__version__)".

repositories/tensorflow/tensorflow/examples/tutorials/monitors$ python -c "import tensorflow; print(tensorflow.__version__)"
1.0.0

If installed from source, provide

The commit hash (git rev-parse HEAD)
The output of bazel version

If possible, provide a minimal reproducible example (We usually don't have time to read hundreds of lines of your code)
repositories/tensorflow/tensorflow/examples/tutorials/monitors$ python iris_monitors.py
Traceback (most recent call last):
File "iris_monitors.py", line 116, in
tf.app.run()
File "/home/chidochipotle/anaconda3/lib/python3.5/site-packages/tensorflow/python/platform/app.py", line 44, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "iris_monitors.py", line 42, in main
tf.contrib.learn.metric_spec.MetricSpec(
AttributeError: module 'tensorflow.contrib.learn' has no attribute 'metric_spec'
What other attempted solutions have you tried?
Logs or other output that would be helpful
(If logs are large, please upload as attachment or provide link).

Needs many more changes to work, iris_monitors.py seems to be is totally broken with r1.0 I am just too newbie to understand what's wrong ...
After fixing a couple of more inaccurate imports I get lots of warnings when I run it:
dkor@panchito:~/repositories/tensorflow/tensorflow/examples/tutorials/monitors$ python iris_monitors.py
WARNING:tensorflow:From /home/dkor/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/monitors.py:322: BaseMonitor.__init__ (from tensorflow.contrib.learn.python.learn.monitors) is deprecated and will be removed after 2016-12-05.
Instructions for updating:
Monitors are deprecated. Please use tf.train.SessionRunHook.
WARNING:tensorflow:From /home/dkor/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/monitors.py:322: BaseMonitor.__init__ (from tensorflow.contrib.learn.python.learn.monitors) is deprecated and will be removed after 2016-12-05.
Instructions for updating:
Monitors are deprecated. Please use tf.train.SessionRunHook.
INFO:tensorflow:Using config: {'_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fdc67b414e0>, '_is_chief': True, '_tf_random_seed': None, '_task_type': None, '_keep_checkpoint_max': 5, '_master': '', '_keep_checkpoint_every_n_hours': 10000, '_save_summary_steps': 100, '_environment': 'local', '_save_checkpoints_secs': 1, '_num_ps_replicas': 0, '_tf_config': gpu_options {
  per_process_gpu_memory_fraction: 1
}
, '_save_checkpoints_steps': None, '_task_id': 0, '_evaluation_master': ''}
/home/dkor/anaconda3/lib/python3.5/site-packages/tensorflow/python/util/deprecation.py:247: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.
  equality = a == b
WARNING:tensorflow:From iris_monitors.py:104: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
  est = Estimator(...) -> est = SKCompat(Estimator(...))
WARNING:tensorflow:From iris_monitors.py:104: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
  est = Estimator(...) -> est = SKCompat(Estimator(...))
WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.
WARNING:tensorflow:From /home/dkor/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:1362: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
INFO:tensorflow:Create CheckpointSaverHook.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
INFO:tensorflow:Saving checkpoints for 851 into /tmp/iris_model/model.ckpt.
INFO:tensorflow:loss = 0.0496295, step = 851
WARNING:tensorflow:From /home/dkor/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/monitors.py:712: calling BaseEstimator.evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
  est = Estimator(...) -> est = SKCompat(Estimator(...))
WARNING:tensorflow:From /home/dkor/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/monitors.py:712: calling BaseEstimator.evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
  est = Estimator(...) -> est = SKCompat(Estimator(...))
WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.
WARNING:tensorflow:From /home/dkor/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:1362: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
INFO:tensorflow:Starting evaluation at 2017-02-17-23:21:20
INFO:tensorflow:Finished evaluation at 2017-02-17-23:21:21
INFO:tensorflow:Saving dict for global step 851: accuracy = 0.966667, auc = 0.998333, global_step = 851, loss = 0.0598002, precision = 1.0, recall = 1.0
WARNING:tensorflow:Skipping summary for global_step, must be a float or np.float32.
INFO:tensorflow:Validation (step 851): precision = 1.0, auc = 0.998333, loss = 0.0598002, recall = 1.0, accuracy = 0.966667, global_step = 851
INFO:tensorflow:Saving checkpoints for 852 into /tmp/iris_model/model.ckpt.
WARNING:tensorflow:From /home/dkor/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/monitors.py:712: calling BaseEstimator.evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
  est = Estimator(...) -> est = SKCompat(Estimator(...))
WARNING:tensorflow:From /home/dkor/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/monitors.py:712: calling BaseEstimator.evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
  est = Estimator(...) -> est = SKCompat(Estimator(...))
WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.
WARNING:tensorflow:From /home/dkor/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:1362: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
INFO:tensorflow:Starting evaluation at 2017-02-17-23:21:23
INFO:tensorflow:Finished evaluation at 2017-02-17-23:21:23
INFO:tensorflow:Saving dict for global step 852: accuracy = 0.966667, auc = 0.998333, global_step = 852, loss = 0.0556784, precision = 1.0, recall = 1.0
WARNING:tensorflow:Skipping summary for global_step, must be a float or np.float32.
INFO:tensorflow:Validation (step 901): precision = 1.0, auc = 0.998333, loss = 0.0556784, recall = 1.0, accuracy = 0.966667, global_step = 852
INFO:tensorflow:Saving checkpoints for 902 into /tmp/iris_model/model.ckpt.
INFO:tensorflow:global_step/sec: 23.6534
INFO:tensorflow:loss = 0.0484522, step = 951
WARNING:tensorflow:From /home/dkor/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/monitors.py:712: calling BaseEstimator.evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
  est = Estimator(...) -> est = SKCompat(Estimator(...))
WARNING:tensorflow:From /home/dkor/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/monitors.py:712: calling BaseEstimator.evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
  est = Estimator(...) -> est = SKCompat(Estimator(...))
WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.
WARNING:tensorflow:From /home/dkor/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:1362: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
INFO:tensorflow:Starting evaluation at 2017-02-17-23:21:24
INFO:tensorflow:Finished evaluation at 2017-02-17-23:21:25
INFO:tensorflow:Saving dict for global step 902: accuracy = 0.966667, auc = 0.998333, global_step = 902, loss = 0.0556355, precision = 1.0, recall = 1.0
WARNING:tensorflow:Skipping summary for global_step, must be a float or np.float32.
INFO:tensorflow:Validation (step 951): precision = 1.0, auc = 0.998333, loss = 0.0556355, recall = 1.0, accuracy = 0.966667, global_step = 902
INFO:tensorflow:Saving checkpoints for 952 into /tmp/iris_model/model.ckpt.
WARNING:tensorflow:From /home/dkor/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/monitors.py:712: calling BaseEstimator.evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
  est = Estimator(...) -> est = SKCompat(Estimator(...))
WARNING:tensorflow:From /home/dkor/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/monitors.py:712: calling BaseEstimator.evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
  est = Estimator(...) -> est = SKCompat(Estimator(...))
WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.
WARNING:tensorflow:From /home/dkor/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:1362: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
INFO:tensorflow:Starting evaluation at 2017-02-17-23:21:26
INFO:tensorflow:Finished evaluation at 2017-02-17-23:21:26
INFO:tensorflow:Saving dict for global step 952: accuracy = 0.966667, auc = 0.998333, global_step = 952, loss = 0.0556713, precision = 1.0, recall = 1.0
WARNING:tensorflow:Skipping summary for global_step, must be a float or np.float32.
INFO:tensorflow:Validation (step 1001): precision = 1.0, auc = 0.998333, loss = 0.0556713, recall = 1.0, accuracy = 0.966667, global_step = 952
INFO:tensorflow:Saving checkpoints for 1002 into /tmp/iris_model/model.ckpt.
INFO:tensorflow:global_step/sec: 31.7257
INFO:tensorflow:loss = 0.0475382, step = 1051
WARNING:tensorflow:From /home/dkor/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/monitors.py:712: calling BaseEstimator.evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
  est = Estimator(...) -> est = SKCompat(Estimator(...))
WARNING:tensorflow:From /home/dkor/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/monitors.py:712: calling BaseEstimator.evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
  est = Estimator(...) -> est = SKCompat(Estimator(...))
WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.
WARNING:tensorflow:From /home/dkor/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:1362: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
INFO:tensorflow:Starting evaluation at 2017-02-17-23:21:27
INFO:tensorflow:Finished evaluation at 2017-02-17-23:21:28
INFO:tensorflow:Saving dict for global step 1002: accuracy = 0.966667, auc = 0.998333, global_step = 1002, loss = 0.0556784, precision = 1.0, recall = 1.0
WARNING:tensorflow:Skipping summary for global_step, must be a float or np.float32.
INFO:tensorflow:Validation (step 1051): precision = 1.0, auc = 0.998333, loss = 0.0556784, recall = 1.0, accuracy = 0.966667, global_step = 1002
INFO:tensorflow:Saving checkpoints for 1052 into /tmp/iris_model/model.ckpt.
WARNING:tensorflow:From /home/dkor/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/monitors.py:712: calling BaseEstimator.evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
  est = Estimator(...) -> est = SKCompat(Estimator(...))
WARNING:tensorflow:From /home/dkor/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/monitors.py:712: calling BaseEstimator.evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
  est = Estimator(...) -> est = SKCompat(Estimator(...))
WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.
WARNING:tensorflow:From /home/dkor/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:1362: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
INFO:tensorflow:Starting evaluation at 2017-02-17-23:21:29
INFO:tensorflow:Finished evaluation at 2017-02-17-23:21:29
INFO:tensorflow:Saving dict for global step 1052: accuracy = 0.966667, auc = 0.998333, global_step = 1052, loss = 0.0557572, precision = 1.0, recall = 1.0
WARNING:tensorflow:Skipping summary for global_step, must be a float or np.float32.
INFO:tensorflow:Validation (step 1101): precision = 1.0, auc = 0.998333, loss = 0.0557572, recall = 1.0, accuracy = 0.966667, global_step = 1052
INFO:tensorflow:Saving checkpoints for 1102 into /tmp/iris_model/model.ckpt.
INFO:tensorflow:global_step/sec: 32.9999
INFO:tensorflow:loss = 0.0466149, step = 1151
WARNING:tensorflow:From /home/dkor/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/monitors.py:712: calling BaseEstimator.evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
  est = Estimator(...) -> est = SKCompat(Estimator(...))
WARNING:tensorflow:From /home/dkor/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/monitors.py:712: calling BaseEstimator.evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
  est = Estimator(...) -> est = SKCompat(Estimator(...))
WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.
WARNING:tensorflow:From /home/dkor/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:1362: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
INFO:tensorflow:Starting evaluation at 2017-02-17-23:21:30
INFO:tensorflow:Finished evaluation at 2017-02-17-23:21:31
INFO:tensorflow:Saving dict for global step 1102: accuracy = 0.966667, auc = 0.998333, global_step = 1102, loss = 0.0559047, precision = 1.0, recall = 1.0
WARNING:tensorflow:Skipping summary for global_step, must be a float or np.float32.
INFO:tensorflow:Validation (step 1151): precision = 1.0, auc = 0.998333, loss = 0.0559047, recall = 1.0, accuracy = 0.966667, global_step = 1102
INFO:tensorflow:Stopping. Best step: 951 with loss = 0.05563554912805557.
INFO:tensorflow:Saving checkpoints for 1151 into /tmp/iris_model/model.ckpt.
INFO:tensorflow:Loss for final step: 0.0466149.
WARNING:tensorflow:From iris_monitors.py:108: calling BaseEstimator.evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
  est = Estimator(...) -> est = SKCompat(Estimator(...))
WARNING:tensorflow:From iris_monitors.py:108: calling BaseEstimator.evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
  est = Estimator(...) -> est = SKCompat(Estimator(...))
WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.
WARNING:tensorflow:From /home/dkor/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:1362: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
INFO:tensorflow:Starting evaluation at 2017-02-17-23:21:32
INFO:tensorflow:Finished evaluation at 2017-02-17-23:21:32
INFO:tensorflow:Saving dict for global step 1151: accuracy = 0.966667, auc = 0.998333, global_step = 1151, loss = 0.0619039
WARNING:tensorflow:Skipping summary for global_step, must be a float or np.float32.
Accuracy: 0.966667
WARNING:tensorflow:From /home/dkor/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/dnn.py:374: calling BaseEstimator.predict (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
  est = Estimator(...) -> est = SKCompat(Estimator(...))
WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.
Predictions: [1, 2]
dkor@panchito:~/repositories/tensorflow/tensorflow/examples/tutorials/monitors$

I'm getting the errors using the latest nightly TF build (3/3/2017) with the Iris dataset. I see this issue as closed. So how was it fixed?
using python 3.5.1 on Windows, no gpu.
code is the same as the example in https://www.tensorflow.org/get_started/tflearn
Errors:
C:\Users\Jake\py\my_env\lib\site-packages\tensorflow\python\util\deprecation.py:248: FutureWarning: comparison to None will result in an elementwise object comparison in the future.
equality = a == b
WARNING:tensorflow:From C:\Users\Jake\py\files\Iris\test1.py:32: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
est = Estimator(...) -> est = SKCompat(Estimator(...))
WARNING:tensorflow:From C:\Users\Jake\py\files\Iris\test1.py:32: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
est = Estimator(...) -> est = SKCompat(Estimator(...))
WARNING:tensorflow:From C:\Users\Jake\py\my_env\lib\site-packages\tensorflow\contrib\learn\python\learn\estimators\head.py:527: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
2017-03-03 09:33:56.774205: W c:\tf_jenkins\home\workspace\nightly-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations.
2017-03-03 09:33:56.774305: W c:\tf_jenkins\home\workspace\nightly-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE2 instructions, but these are available on your machine and could speed up CPU computations.
2017-03-03 09:33:56.775876: W c:\tf_jenkins\home\workspace\nightly-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
2017-03-03 09:33:56.776302: W c:\tf_jenkins\home\workspace\nightly-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-03-03 09:33:56.776872: W c:\tf_jenkins\home\workspace\nightly-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-03-03 09:33:56.777305: W c:\tf_jenkins\home\workspace\nightly-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-03-03 09:33:56.777783: W c:\tf_jenkins\home\workspace\nightly-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-03-03 09:33:56.778242: W c:\tf_jenkins\home\workspace\nightly-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
WARNING:tensorflow:From C:\Users\Jake\py\my_env\lib\site-packages\tensorflow\python\util\deprecation.py:335: calling DNNClassifier.predict (from tensorflow.contrib.learn.python.learn.estimators.dnn) with outputs=None is deprecated and will be removed after 2017-03-01.
Instructions for updating:
Please switch to predict_classes, or set outputs argument.
WARNING:tensorflow:From C:\Users\Jake\py\my_env\lib\site-packages\tensorflow\contrib\learn\python\learn\estimators\dnn.py:395: calling BaseEstimator.predict (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.
Instructions for updating:
Estimator is decoupled from Scikit Learn interface by moving into
separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion:
est = Estimator(...) -> est = SKCompat(Estimator(...))
WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.
Predictions: [1, 2, 0]

Use learn.MetricSpec instead

I'm new to this and using the tensorflow site as a learning resource. If recent code changes break the learn example than for the sake of clear/concise learning it would require updating the tensorflow site with the corrected code. Specifically: https://www.tensorflow.org/get_started/tflearn


[AutoRelease] t2-security-2024-01-19-75246(can only be merged by SDK owner)

https://github.com/Azure/sdk-release-request/issues/4898
Live test success
https://dev.azure.com/azure-sdk/internal/_build?definitionId=1028

issue link:https://github.com/Azure/sdk-release-request/issues/4898

/check-enforcer override


pythonPackages.pdf2image: init at 0.1.13

Motivation for this change
Inits python package pdf2image at version 0.1.13.
Things done

[ ] Tested using sandboxing (nix.useSandbox on NixOS, or option sandbox in nix.conf on non-NixOS)
Built on platform(s)

[x] NixOS
[ ] macOS
[ ] other Linux distributions

[ ] Tested via one or more NixOS test(s) if existing and applicable for the change (look inside nixos/tests)
[x] Tested compilation of all pkgs that depend on this change using nix-shell -p nox --run "nox-review wip"
[ ] Tested execution of all binary files (usually in ./result/bin/)
[x] Fits CONTRIBUTING.md.

@GrahamcOfBorg build pythonPackages.pdf2image python3Packages.pdf2image

Success on x86_64-linux (full log)
Attempted: pythonPackages.pdf2image, python3Packages.pdf2image
Partial log (click to expand)
warning: no files found matching 'LICENSE.txt'
writing manifest file 'pdf2image.egg-info/SOURCES.txt'
running build_ext

----------------------------------------------------------------------
Ran 0 tests in 0.000s

OK
/nix/store/h7kn32gbxdh8bwcssmr9fz0ab0vg7xgp-python2.7-pdf2image-0.1.13
/nix/store/9jjwh4vfbdahfs3rr70p8048j4jlp5l1-python3.6-pdf2image-0.1.13

Success on aarch64-linux (full log)
Attempted: pythonPackages.pdf2image, python3Packages.pdf2image
Partial log (click to expand)
warning: no files found matching 'LICENSE.txt'
writing manifest file 'pdf2image.egg-info/SOURCES.txt'
running build_ext

----------------------------------------------------------------------
Ran 0 tests in 0.000s

OK
/nix/store/wwywqbgbpk618fny2gn07a89myxbk9ax-python2.7-pdf2image-0.1.13
/nix/store/3kgd8vw6liryh3m0agn2glhzd560w5h3-python3.6-pdf2image-0.1.13

Failure on x86_64-darwin (full log)
Attempted: pythonPackages.pdf2image, python3Packages.pdf2image
Partial log (click to expand)
                                                              ^
1 error generated.
[160/219] Linking CXX executable utils/pdffonts
[161/219] Building CXX object utils/CMakeFiles/pdfdetach.dir/parseargs.cc.o
[162/219] Building CXX object utils/CMakeFiles/pdfunite.dir/pdfunite.cc.o
ninja: build stopped: subcommand failed.
builder for '/nix/store/q86lvqzx0jrbpcj7zq02k3jbw34gn2mw-poppler-utils-0.65.0.drv' failed with exit code 1
cannot build derivation '/nix/store/b7n81480qhnk1lnc9b3vjr91mjza8clp-python2.7-pdf2image-0.1.13.drv': 1 dependencies couldn't be built
cannot build derivation '/nix/store/wfx5ww0lw4bc0d27rdd92fbhngwc1ckh-python3.6-pdf2image-0.1.13.drv': 1 dependencies couldn't be built
error: build of '/nix/store/b7n81480qhnk1lnc9b3vjr91mjza8clp-python2.7-pdf2image-0.1.13.drv', '/nix/store/wfx5ww0lw4bc0d27rdd92fbhngwc1ckh-python3.6-pdf2image-0.1.13.drv' failed

The problem seems to be, that poppler_utils fails to build on darwin..

@ttuegel cc

@ttuegel cc

poppler_utils failure on Darwin is a separate issue, see #41765


Add cosmos events to handler

Note: This PR is pointing to  cosmos:musnit/update_new_sdk and ideally that branch should be reviewed and merged first

@musnit just rebased the PR to master. Could you merge it and resolve the conflicts?

@musnit can you add the Changelog entry too as described there?


Language restrictions

I have had lots of problems getting AssignedAccess to work on a swedish OS (ShellExperienceHost cannot start).
When applying on a english OS everything works fine.
Is there any documented problems with non-english OS? If so it should be stated somewhere here.

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

ID: 78038d67-04a1-f4ae-6a69-98e157688a31
Version Independent ID: c86b4460-e8be-8537-a6c8-3cb094ef14a3
Content: Set up a multi-app kiosk  (Windows 10)
Content Source: windows/configuration/lock-down-windows-10-to-specific-apps.md
Product: w10
GitHub Login: @jdeckerMS
Microsoft Alias: jdecker

Same here: Multi-app kiosk mode doesn't work anymore with German Windows 1809 installations. User is simply logged out after login :-(
With 1803 and 1709 it worked just fine.
Any ideas? We urgently need it for our customers so that they can update to 1809!

Same here: Multi-app kiosk mode doesn't work anymore with German Windows 1809 installations. User is simply logged out after login :-(
With 1803 and 1709 it worked just fine.
Any ideas? We urgently need it for our customers so that they can update to 1809!

I found the issue and a workaround.
It seems that AssignedAccess cannot understand localized groupnames and for some reason it needs to find "Administrators". This is still a problem that needs to be resolved but you can simple create the group during provisioning and add it to your localized administrators-group.
It's a dirty solution but it works.

Wow - thanks a lot for your quick replay and the dirty solution that really does the trick!
 But at least we now know how to work around this until it is fixed.

@mortinious and @tlange-tech2go  I've checked with the product team, and this issue has been fixed in the next feature update to Windows 10. I don't know if they'll push the fix to current and earlier versions of Windows 10 and when that might be, if I get additional info I'll let you know.


E upgader

Integration Upgrader package to update application. Added some driver test codes to test_driver/app_test.dart file for upgrade alert.

It is not last version...


update tests so they run on  openshift 4.2

a few changes to tests to make them run on 4.2
fixes #2049
issues were:

image stream java:8 is not latest https://github.com/openshift/odo/issues/2049#issuecomment-525768485
image stream dotnet:2.0 not available

/retest
[odo]  ✗  Unable to connect to OpenShift cluster, is it down?

/retest
2019/09/02 13:14:01 error: unable to signal to artifacts container to terminate in pod e2e-scenarios, triggering deletion: could not run remote command: container artifacts is not valid for pod e2e-scenarios
2019/09/02 13:14:01 error: unable to retrieve artifacts from pod e2e-scenarios: could not read gzipped artifacts: container artifacts is not valid for pod e2e-scenarios
2019/09/02 13:14:06 error: could not wait for pod 'e2e-scenarios': it is no longer present on the cluster (usually a result of a race or resource pressure. re-running the job should help)
2019/09/02 13:14:07 Ran for 39m51s
2019/09/02 13:14:07 Submitted failure event to sentry (id=f56f0826d8914a4c8171d1714d70651c)
error: could not run steps: step e2e-scenarios failed: template pod "e2e-scenarios" failed: pod was deleted while ci-operator step was waiting for it

/retest
[odo]  ✗  Unable to connect to OpenShift cluster, is it down?

/retest
2019/09/02 14:04:19 error: unable to signal to artifacts container to terminate in pod integration, triggering deletion: could not run remote command: container artifacts is not valid for pod integration
2019/09/02 14:04:19 error: unable to retrieve artifacts from pod integration: could not read gzipped artifacts: container artifacts is not valid for pod integration
2019/09/02 14:04:24 error: could not wait for pod 'integration': it is no longer present on the cluster (usually a result of a race or resource pressure. re-running the job should help)
2019/09/02 14:04:25 Ran for 1h30m20s
2019/09/02 14:04:25 Submitted failure event to sentry (id=1b053eecc2cd49488f9ab3838a593ba1)
error: could not run steps: step integration failed: template pod "integration" failed: pod was deleted while ci-operator step was waiting for it

/test benchmark
/test integration

[odo]  ✗  Failed To Update Config To Component Deployed
[odo]  ✗  Applying configuration [5m]

/retest

2019/09/03 10:04:23 error: unable to signal to artifacts container to terminate in pod integration, triggering deletion: could not run remote command: container artifacts is not valid for pod integration
2019/09/03 10:04:23 error: unable to retrieve artifacts from pod integration: could not read gzipped artifacts: container artifacts is not valid for pod integration
2019/09/03 10:04:27 error: could not wait for pod 'integration': it is no longer present on the cluster (usually a result of a race or resource pressure. re-running the job should help)
E0903 10:04:27.803140      14 event.go:191] Server rejected event '&v1.Event{TypeMeta:v1.TypeMeta{Kind:"", APIVersion:""}, ObjectMeta:v1.ObjectMeta{Name:".15c0e58ddddb3bec", GenerateName:"", Namespace:"ci-op-00nzk9zs", SelfLink:"", UID:"", ResourceVersion:"", Generation:0, CreationTimestamp:v1.Time{Time:time.Time{wall:0x0, ext:0, loc:(*time.Location)(nil)}}, DeletionTimestamp:(*v1.Time)(nil), DeletionGracePeriodSeconds:(*int64)(nil), Labels:map[string]string(nil), Annotations:map[string]string(nil), OwnerReferences:[]v1.OwnerReference(nil), Initializers:(*v1.Initializers)(nil), Finalizers:[]string(nil), ClusterName:"", ManagedFields:[]v1.ManagedFieldsEntry(nil)}, InvolvedObject:v1.ObjectReference{Kind:"", Namespace:"ci-op-00nzk9zs", Name:"", UID:"", APIVersion:"", ResourceVersion:"", FieldPath:""}, Reason:"CiJobFailed", Message:"Running job pull-ci-openshift-odo-master-integration for PR https://github.com/openshift/odo/pull/2059 in namespace ci-op-00nzk9zs from author kadel", Source:v1.EventSource{Component:"ci-op-00nzk9zs", Host:""}, FirstTimestamp:v1.Time{Time:time.Time{wall:0xbf53ac8aefb24dec, ext:5493845526774, loc:(*time.Location)(0x26765e0)}}, LastTimestamp:v1.Time{Time:time.Time{wall:0xbf53ac8aefb24dec, ext:5493845526774, loc:(*time.Location)(0x26765e0)}}, Count:1, Type:"Warning", EventTime:v1.MicroTime{Time:time.Time{wall:0x0, ext:0, loc:(*time.Location)(nil)}}, Series:(*v1.EventSeries)(nil), Action:"", Related:(*v1.ObjectReference)(nil), ReportingController:"", ReportingInstance:""}': 'events ".15c0e58ddddb3bec" is forbidden: unable to create new content in namespace ci-op-00nzk9zs because it is being terminated' (will not retry!)
2019/09/03 10:04:28 Ran for 1h31m34s
2019/09/03 10:04:29 Submitted failure event to sentry (id=53f06f8b55764623a68a9a022dc0b3ad)

/retest

/lgtm

/test benchmark

/lgtm cancel

/lgtm


feat(forge): "--exact" flag for matching tests

Component
Forge
Describe the feature you would like
The flags --match-contract and --match-test are globs, which means that they will match two similar test function names like these:

testFoo
testFooBar

However, developers often need to focus on one function in particular. It would be helpful to have another flag --exact that, when activated, would not match the testFooBar function when the user passes testFoo to --match-test.
Alternatively, we may consider two separate flags --match-exact-contract and --match-exact-test, which would achieve the same thing with a single flag rather than two.
Additional context
No response

Workaround: forge test --mt testFoo\\b

Workaround: forge test --mt testFoo\\b

That works!! But
Why testFoo$ or ^testFoo$ dont work?
Need activate multiline or something like that?

$ does not work because it matches the end of a string, where as \b matches word boundaries. The match test flag uses regex, so you can match exact names using that approach. As a result I think this issue can be closed since it's already supported.


feat: add prim call_raw : (Principal, Text, Blob) -> async Blob

Fixes #2703 (by lowering ourselves to the level of Rust).
Adds a prim to dynamically invoke a method by name with an already serialized blob and get the raw (undeserialized) blob back asynchronously:
call_raw : (canister : Principal, function_name : Text, arg : Blob) -> async Blob

The function can only be called in an asynchronous context and this is enforced by the type system.
There is no assumption that the contents of either blob is Candid, so this could also be used to talk to non-Candid endpoints.
This should be sufficient to implement the call-forwarding functionality of the Rust cycles wallet.

[ ] Determine whether the method name must be (rope)-normalized before use. Currently it is not. @nomeata,  what's the representation invariant for ordinary shared functions - I see they are (Principal,Text) pairs, but is the Text normalized?
[ ] Any ideas for better name: request, send, call, invoke, call_dynamic spring to mind

Determine whether the method name must be (rope)-normalized before use. Currently it is not. @nomeata, what's the representation invariant for ordinary shared functions - I see they are (Principal,Text) pairs, but is the Text normalized?

The new call_raw takes a Text, and you certainly need to make that contiguous before passing it to the system API. I'm not sure how shared functions representation play a role here?

Ah, I see why, because you construct such a function ref pair in the backend.
I don't think it's clearly defined what the representation is yet, presumably, so far only flat text values occurred. So the value needs to flattened at the right place…

There is no assumption that the contents of either blob is Candid, so this could also be used to talk to non-Candid endpoints.

For the record, I'm really concerned about the slippery slope this creates towards leaking and breaking more abstractions.

Maybe there is a way to close the abstraction later? This is exactly what we need right now to develop some things on our road map using motoko that have been blocked since launch. Thank you!

There is no assumption that the contents of either blob is Candid, so this could also be used to talk to non-Candid endpoints.

For the record, I'm really concerned about the slippery slope this creates towards leaking and breaking more abstractions.

Objection noted.
However, we had promised a solution for ages and never delivered one, so offering this as an experimental feature while we come up with something better seems reasonable to me.  Given that you can already achieve this (modulo a change in caller) by bouncing off Rust, I don't really see it compromising more than we have already across the company.


Import cleaning

Improves handling of optional dependencies including on installation, and will hopefully remove relative and star imports once I am done

Also waiting on #92 so I can make sure ebcc is handled consistently

Codecov Report
Patch coverage: 98.77% and project coverage change: -0.03 :warning:

Comparison is base (be22731) 71.72% compared to head (ffbaf95) 71.70%.

:exclamation: Your organization is not using the GitHub App Integration. As a result you may experience degraded service beginning May 15th. Please install the Github App Integration for your organization. Read more.
Additional details and impacted files
@@            Coverage Diff             @@
##           master      #96      +/-   ##
==========================================
- Coverage   71.72%   71.70%   -0.03%
==========================================
  Files         133      133
  Lines       18460    18459       -1
  Branches     2573     2514      -59
==========================================
- Hits        13241    13236       -5
- Misses       4486     4495       +9
+ Partials      733      728       -5

Impacted Files
Coverage Δ

vayesta/core/symmetry/symmetry.py
0.00% <0.00%> (ø)

vayesta/dmet/__init__.py
66.66% <66.66%> (-3.93%)
:arrow_down:

vayesta/__init__.py
76.53% <100.00%> (ø)

vayesta/core/ao2mo/__init__.py
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vayesta/core/ao2mo/postscf_ao2mo.py
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vayesta/core/bath/__init__.py
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vayesta/core/bath/bno.py
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vayesta/core/bath/ewdmet.py
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vayesta/core/bath/full.py
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... and 48 more

... and 7 files with indirect coverage changes

:umbrella: View full report in Codecov by Sentry.
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I would say relative imports are OK as long as they are explicit (with a leading .)
and a limited number of star imports are OK, if the imported module defines __all__.
What's the reason to get rid of them?

PEP 8 recommends against relative imports:

Relative imports for intra-package imports are highly discouraged. Always use the absolute package path for all imports.

Absolute imports make it easier to avoid circular importing issues in complex packages.
As for star imports, PEP 8 does not explicitly mention them as far as I know, however in my opinion (and I think it's a common opinion) they're a bad idea. Not only do they pollute the namespace of the module, but they make it extremely difficult to chase down the origin of imported classes and methods, and generally reduce readability. They also prevent some static analysis and flaking tools on top of that.
I think in both cases, given that you can achieve the same behaviour with explicit and absolute imports, the latter is just the better option.

I certainly agree that following an execution thread of someone else's code is more difficult with star imports. But maybe thats because I'm still not using an IDE...

@obackhouse Where did you find this quote in PEP 8? Was it maybe a Python 2 version, before we had the dot-syntax?
https://peps.python.org/pep-0008/#imports says

However, explicit relative imports are an acceptable alternative to absolute imports, especially when dealing with complex package layouts where using absolute imports would be unnecessarily verbose

I don't have strong feelings about the star import, I agree they should generally be avoided.
Side note @ghb24 I now switched to PyCharm after so many years of vim it's the best thing ever :grin:  (just install ideavim to keep the commands)

I think there is a decent benefit to consistency in our code, and I would err on the side of absolute imports just for clarity's sake. While relative imports are definitely sometimes easier to write, given the plethora of tools to allow automatic conversion between relative imports and absolute imports (including in PyCharm, which I'm glad you've finally come around to using!).

@obackhouse : Conflicts now with updated master...

@obackhouse : Conflicts now with updated master...

I'll sort it, don't think I'm done just yet anyway

Good to merge now


pytorch multipoint regression model cards

Hi!
I went over the notebooks and some of the code for the Model Card Generator, but it seems that all three examples are classifiers.
What to do in case where I would like to create a model card for a multi-point regressor?
For example, let's assume that we have a torch model that outputs 21 2-D facial landmarks.
Let's say that I would like to show that NME(normalized mean error) is the same e.g., across all geos or races.
The make_eval_dataframe() method in model_card_gen/intel_ai_safety/model_card_gen/analyze/torch_analyzer.py assumes that there's a single numerical column representing labels and predictions so that probably wouldn't work for 21x2 numbers.
Any hints on how to tackle this kind of model with your toolkit?

@mkurczew Thanks for the comment! We had a similar issue with multi-label support with pytorch. The workaround we received can be found here: https://github.com/tensorflow/model-analysis/issues/162
In short i think this could be solvable by creating a custom beam.Pipeline to generate evaluation results.


Bundle autoupdates for beginning a speech

@normanjaeckel I have to merge it today. Please review later. Thanks.

Ok.


Prompt typing area is partly obscured by horizontal scroll bar

Not a big deal, but the default size of the prompt typing area is consistently too small, on both Chrome and FireFox. Even though the default seems to be exactly the size of a single line of text (which is fine), the horizontal scroll bar makes makes it difficult to see everything one is typing.

Maybe just a little more buffer around the line would make the text more visible?

As of yet, I have been unable to replicate this (using Edge, Chrome, Firefox, or Safari) and that's making it a little hard to determine the correct style updates. I am going to see if I can get 30 mins on the calendar next week for us to look at this together briefly.

Explicitly setting the horizontal scrollbar on the textarea not to display (via overflow-y: hidden) in an attempt to solve this. Should go out in next release.

I just did a manual deploy from main with the latest changes. In theory, next time you use the app and reload you should be on v0.21.1 with this change in place and we'll know if it improved anything or not.

I'm looking at v0.21.1 and can confirm that this seems to have fixed the problem for me.

Thanks for confirming!


Cognito - username is mail instead of sub after passwordless login (customAuthChallenge)

i use the usersCreateAuthChallenge / usersDefineAuthChallenge ...
When i create the user, the username is the sub from cognito. So in the most example code from amplify doc is the username used for calls.
so now when i log in with the customAuthChallenge, the user is logged in, but the cognito username is now the mail of the user instead of the sub.

@SwaySway
As WorkAround i use now user.attributes.sub instead of user.username.
But in the AppSync Resolver i found this:
#set( $identityValue = $util.defaultIfNull($ctx.identity.claims.get("username"), $util.defaultIfNull($ctx.identity.claims.get("cognito:username"), "___xamznone____")) )
So i guess we need to fix this correct with the username?!

FYI:
So i checked an other Lambda (called per Appsync - Amplify GraphQL).
In my DoSomeThingsLambda the identity.claims.username is correct username == sub.
So in FrontEnd username == mail
When i call a Lambda per AppSync, it seems that username is == sub.

Closing due to lack of response.