Installing and running Tensorflow 1.0

I have just installed tensorflow 1 and when I try to run a program I get following errors.
"C:\Users\CRCV>python a.py
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "BestSplits" device_type: "CPU"') for unknown op: BestSplits
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "CountExtremelyRandomStats" device_type: "CPU"') for unknown op: CountExtremelyRandomStats
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "FinishedNodes" device_type: "CPU"') for unknown op: FinishedNodes
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "GrowTree" device_type: "CPU"') for unknown op: GrowTree
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "ReinterpretStringToFloat" device_type: "CPU"') for unknown op: ReinterpretStringToFloat
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "SampleInputs" device_type: "CPU"') for unknown op: SampleInputs
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "ScatterAddNdim" device_type: "CPU"') for unknown op: ScatterAddNdim
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "TopNInsert" device_type: "CPU"') for unknown op: TopNInsert
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "TopNRemove" device_type: "CPU"') for unknown op: TopNRemove
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "TreePredictions" device_type: "CPU"') for unknown op: TreePredictions
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "UpdateFertileSlots" device_type: "CPU"') for unknown op: UpdateFertileSlots
b'hello tf'"
Any help?
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Was this code made in previous TensorFlow version?

This is a dupe of #7500. Apparently this issue is fixed at HEAD but didn't make it to the release build. Upgrading to latest nightly build could work as answered by @mrry.

No, I have made new installation and then was trying to print hello message
On Wed, Feb 15, 2017 at 4:27 PM, Adriano Carmezim<EMAIL_ADDRESS>wrote:

Was this code made in previous TensorFlow version?
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@sajjo79 Please see #7500, the nightly build referred above worked.

Thanks @Carmezim, @sajjo79 let us know if this solves the problem.

Automatically closing due to lack of recent activity. Please update the issue when new information becomes available, and we will reopen the issue. Thanks!

This still appears to be an issue. Just installed TensorFlow 1.0 on Windows 10 (x64) following the instructions from here: https://www.tensorflow.org/install/install_windows. It sounds like whatever is in the nightly build referenced in this post has not yet made it to the official version of 1.0 that's available via:
pip3 install --upgrade tensorflow
Here's what I'm seeing just after installation:
`~\Projects> python
Python 3.5.2 (v3.5.2:4def2a2901a5, Jun 25 2016, 22:18:55) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.

import tensorflow as tf;
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "BestSplits" device_type: "CPU"') for unknown op: BestSplits
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "CountExtremelyRandomStats" device_type: "CPU"') for unknown op: CountExtremelyRandomStats
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "FinishedNodes" device_type: "CPU"') for unknown op: FinishedNodes
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "GrowTree" device_type: "CPU"') for unknown op: GrowTree
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "ReinterpretStringToFloat" device_type: "CPU"') for unknown op: ReinterpretStringToFloat
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "SampleInputs" device_type: "CPU"') for unknown op: SampleInputs
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "ScatterAddNdim" device_type: "CPU"') for unknown op: ScatterAddNdim
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "TopNInsert" device_type: "CPU"') for unknown op: TopNInsert
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "TopNRemove" device_type: "CPU"') for unknown op: TopNRemove
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "TreePredictions" device_type: "CPU"') for unknown op: TreePredictions
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "UpdateFertileSlots" device_type: "CPU"') for unknown op: UpdateFertileSlots
b'Hello, TensorFlow!'
`

I had a similar problem, If I run the print a second time the issue seems to go away, but it it reproducible.
I installed tensor flow using this command: pip3 install --upgrade tensorflow
from these instructions: https://www.tensorflow.org/install/install_windows
Python 3.5.3 (v3.5.3:1880cb95a742, Jan 16 2017, 16:02:32) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.

import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "BestSplits" device_type: "CPU"') for unknown op: BestSplits
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "CountExtremelyRandomStats" device_type: "CPU"') for unknown op: CountExtremelyRandomStats
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "FinishedNodes" device_type: "CPU"') for unknown op: FinishedNodes
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "GrowTree" device_type: "CPU"') for unknown op: GrowTree
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "ReinterpretStringToFloat" device_type: "CPU"') for unknown op: ReinterpretStringToFloat
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "SampleInputs" device_type: "CPU"') for unknown op: SampleInputs
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "ScatterAddNdim" device_type: "CPU"') for unknown op: ScatterAddNdim
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "TopNInsert" device_type: "CPU"') for unknown op: TopNInsert
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "TopNRemove" device_type: "CPU"') for unknown op: TopNRemove
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "TreePredictions" device_type: "CPU"') for unknown op: TreePredictions
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "UpdateFertileSlots" device_type: "CPU"') for unknown op: UpdateFertileSlots
b'Hello, TensorFlow!'
print(sess.run(hello))
b'Hello, TensorFlow!'
print(tf.version)
1.0.1

@JacobChrist This issue is fixed at HEAD so you can ignore it or install 1.1r

I have the same error I just install tensorflow with this commands

pip3 install --upgrade tensorflow

and I type this code from tensorflow get started example code TensorFlow beginers
 import tensorflow as tf

hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))

Look at first line i have the output that i want the problem is i have all this errors ??
THE ERROR
b'Hello, TensorFlow!'
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "BestSplits" device_type: "CPU"') for unknown op: BestSplits
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "CountExtremelyRandomStats" device_type: "CPU"') for unknown op: CountExtremelyRandomStats
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "FinishedNodes" device_type: "CPU"') for unknown op: FinishedNodes
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "GrowTree" device_type: "CPU"') for unknown op: GrowTree
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "ReinterpretStringToFloat" device_type: "CPU"') for unknown op: ReinterpretStringToFloat
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "SampleInputs" device_type: "CPU"') for unknown op: SampleInputs
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "ScatterAddNdim" device_type: "CPU"') for unknown op: ScatterAddNdim
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "TopNInsert" device_type: "CPU"') for unknown op: TopNInsert
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "TopNRemove" device_type: "CPU"') for unknown op: TopNRemove
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "TreePredictions" device_type: "CPU"') for unknown op: TreePredictions
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "UpdateFertileSlots" device_type: "CPU"') for unknown op: UpdateFertileSlots

Hi @George35mk, could you try pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.0.1-cp35-cp35m-win_amd64.whl

Thnx Carmezin but I have the same error
I install python 3.6 and I find that the tensorflow is not worked with that version after I make a research I find that I need 3.5 and I install the new python. I check the ENV on my system and I use the 3.5 pthon
what are you think ??
b'Hello, TensorFlow!'
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "BestSplits" device_type: "CPU"') for unknown op: BestSplits
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "CountExtremelyRandomStats" device_type: "CPU"') for unknown op: CountExtremelyRandomStats
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "FinishedNodes" device_type: "CPU"') for unknown op: FinishedNodes
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "GrowTree" device_type: "CPU"') for unknown op: GrowTree
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "ReinterpretStringToFloat" device_type: "CPU"') for unknown op: ReinterpretStringToFloat
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "SampleInputs" device_type: "CPU"') for unknown op: SampleInputs
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "ScatterAddNdim" device_type: "CPU"') for unknown op: ScatterAddNdim
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "TopNInsert" device_type: "CPU"') for unknown op: TopNInsert
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "TopNRemove" device_type: "CPU"') for unknown op: TopNRemove
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "TreePredictions" device_type: "CPU"') for unknown op: TreePredictions
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "UpdateFertileSlots" device_type: "CPU"') for unknown op: UpdateFertileSlots

That hide the error but i think is not the best solution
import tensorflow as tf
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
print('TensorFlow version: {0}'.format(tf.__version__))
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))

i gona install the gpu version

This issue is fixed at HEAD and in r1.1, I think the url on the site (the one given above) needs to be updated (from 1.0.1 to 1.1.0), could you download the whl from PYPI and install it locally?
The reason why you're not seeing the ERROR logs anymore is due TF_CPP_MIN_LOG_LEVEL env. variable when set to 3 silence all logs, which is not recommended because you'll won't see any ERROR logs.

I have downloaded the
tensorflow-1.1.0rc2-cp35-cp35m-win_amd64.whl (md5) for windows is ok
now how to instaled ??

https://www.youtube.com/watch?v=MzV4N4XUvYc

bro I give up I spend 5 hours with that tensorflow maybe in the future I come again look at the errors I have
Thanx for your help Carmezim
TensorFlow version: 1.1.0-rc2
b'Hello, TensorFlow!'
2017-04-16 19:03:52.856725: W c:\tf_jenkins\home\workspace\release-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.
2017-04-16 19:03:52.856766: W c:\tf_jenkins\home\workspace\release-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.
2017-04-16 19:03:52.856775: W c:\tf_jenkins\home\workspace\release-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.
2017-04-16 19:03:52.856784: W c:\tf_jenkins\home\workspace\release-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.
2017-04-16 19:03:52.856791: W c:\tf_jenkins\home\workspace\release-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.
2017-04-16 19:03:52.856798: W c:\tf_jenkins\home\workspace\release-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.
2017-04-16 19:03:52.856804: W c:\tf_jenkins\home\workspace\release-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.
2017-04-16 19:03:52.856811: W c:\tf_jenkins\home\workspace\release-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.

Download the nightly build, it will work http://ci.tensorflow.org/view/Nightly/job/nightly-win/146/
The nightly builds are the one common solution that worked for everyone with OkKernels error logs.

Hi @mrry, apparently the site urls and PYPI are using a previous TF without the fix for the above, should I open an issue to track this?

again the same error is not working

@George35mk that is odd though, this is the last build and the fix added a long time ago. This issue is a duplicate of #7500. Did you make sure to uninstall all TF installations in your machine before installing the new one?
If you are sure you did the correct steps to make clean install with the nightly build I'll wait if @mrry has any insight on this.

@George35mk I just downloaded and installed the nightly build and is running successfully. It is something on your side
c:\Users\Downloads>pip install tensorflow-1.1.0rc1-cp35-cp35m-win_amd64.whl
Processing c:\users\downloads\tensorflow-1.1.0rc1-cp35-cp35m-win_amd64.whl
Requirement already satisfied: numpy>=1.11.0 in c:\users\appdata\local\programs\python\python35\lib\site-packages (from tensorflow==1.1.0rc1)
Requirement already satisfied: markdown==2.2.0 in c:\users\appdata\local\programs\python\python35\lib\site-packages (from tensorflow==1.1.0rc1)
Requirement already satisfied: six>=1.10.0 in c:\users\appdata\local\programs\python\python35\lib\site-packages (from tensorflow==1.1.0rc1)
Requirement already satisfied: protobuf>=3.2.0 in c:\users\appdata\local\programs\python\python35\lib\site-packages (from tensorflow==1.1.0rc1)
Requirement already satisfied: html5lib==0.9999999 in c:\users\appdata\local\programs\python\python35\lib\site-packages (from tensorflow==1.1.0rc1)
Requirement already satisfied: werkzeug>=0.11.10 in c:\users\appdata\local\programs\python\python35\lib\site-packages (from tensorflow==1.1.0rc1)
Requirement already satisfied: bleach==1.5.0 in c:\users\appdata\local\programs\python\python35\lib\site-packages (from tensorflow==1.1.0rc1)
Requirement already satisfied: wheel>=0.26 in c:\users\appdata\local\programs\python\python35\lib\site-packages (from tensorflow1.1.0rc1)
Requirement already satisfied: setuptools in c:\users\appdata\local\programs\python\python35\lib\site-packages (from protobuf>=3.2.0->tensorflow==1.1.0rc1)
Requirement already satisfied: packaging>=16.8 in c:\users\appdata\local\programs\python\python35\lib\site-packages (from setuptools->protobuf>=3.2.0->tensorflow==1.1.0rc1)
Requirement already satisfied: appdirs>=1.4.0 in c:\users\appdata\local\programs\python\python35\lib\site-packages (from setuptools->protobuf>=3.2.0->tensorflow==1.1.0rc1)
Requirement already satisfied: pyparsing in c:\users\appdata\local\programs\python\python35\lib\site-packages (from packaging>=16.8->setuptools->protobuf>=3.2.0->tensorflow==1.1.0rc1)
Installing collected packages: tensorflow
Successfully installed tensorflow-1.1.0rc1

c:\Users\Downloads>python
Python 3.5.3 (v3.5.3:1880cb95a742, Jan 16 2017, 16:02:32) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
>>> print(sess.run(hello))
b'Hello, TensorFlow!'

i open the directory
C:\Users\George\AppData\Local\Programs\Python\Python35\Lib\site-packages
and I saw tensorflow folder and the  tensorflow-1.1.0rc1.dist-info folder
i run pip uninstall tensorflow and is uninstall everything related to tensorflow

all happens when i run sess = tf.constant('Hello world')

C:\Users\George\AppData\Local\Programs\Python\Python35>py -3.5 -m install tensorflow-1.1.0rc1-cp35-cp35m-win_amd64.whl
C:\Users\George\AppData\Local\Programs\Python\Python35\python.exe: No module named install

C:\Users\George\AppData\Local\Programs\Python\Python35>py -3.5 -m pip install tensorflow-1.1.0rc1-cp35-cp35m-win_amd64.whl
Processing c:\users\george\appdata\local\programs\python\python35\tensorflow-1.1.0rc1-cp35-cp35m-win_amd64.whl
Requirement already satisfied: wheel>=0.26 in c:\users\george\appdata\local\programs\python\python35\lib\site-packages (from tensorflow==1.1.0rc1)
Requirement already satisfied: bleach==1.5.0 in c:\users\george\appdata\local\programs\python\python35\lib\site-packages (from tensorflow==1.1.0rc1)
Requirement already satisfied: six>=1.10.0 in c:\users\george\appdata\local\programs\python\python35\lib\site-packages (from tensorflow==1.1.0rc1)
Requirement already satisfied: html5lib==0.9999999 in c:\users\george\appdata\local\programs\python\python35\lib\site-packages (from tensorflow==1.1.0rc1)
Requirement already satisfied: protobuf>=3.2.0 in c:\users\george\appdata\local\programs\python\python35\lib\site-packages (from tensorflow==1.1.0rc1)
Requirement already satisfied: numpy>=1.11.0 in c:\users\george\appdata\local\programs\python\python35\lib\site-packages (from tensorflow==1.1.0rc1)
Requirement already satisfied: werkzeug>=0.11.10 in c:\users\george\appdata\local\programs\python\python35\lib\site-packages (from tensorflow==1.1.0rc1)
Requirement already satisfied: markdown==2.2.0 in c:\users\george\appdata\local\programs\python\python35\lib\site-packages (from tensorflow==1.1.0rc1)
Requirement already satisfied: setuptools in c:\users\george\appdata\local\programs\python\python35\lib\site-packages (from protobuf>=3.2.0->tensorflow==1.1.0rc1)
Requirement already satisfied: appdirs>=1.4.0 in c:\users\george\appdata\local\programs\python\python35\lib\site-packages (from setuptools->protobuf>=3.2.0->tensorflow==1.1.0rc1)
Requirement already satisfied: packaging>=16.8 in c:\users\george\appdata\local\programs\python\python35\lib\site-packages (from setuptools->protobuf>=3.2.0->tensorflow==1.1.0rc1)
Requirement already satisfied: pyparsing in c:\users\george\appdata\local\programs\python\python35\lib\site-packages (from packaging>=16.8->setuptools->protobuf>=3.2.0->tensorflow==1.1.0rc1)
Installing collected packages: tensorflow
Successfully installed tensorflow-1.1.0rc1

C:\Users\George\AppData\Local\Programs\Python\Python35>
C:\Users\George\AppData\Local\Programs\Python\Python35>python
Python 3.5.2 (v3.5.2:4def2a2901a5, Jun 25 2016, 22:18:55) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, Tensorflow')
>>> sess = tf.Session()
2017-04-16 20:31:25.726866: 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.
2017-04-16 20:31:25.729367: 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.
2017-04-16 20:31:25.730882: 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.
2017-04-16 20:31:25.731601: 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.
2017-04-16 20:31:25.732195: 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.
2017-04-16 20:31:25.733119: 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.
2017-04-16 20:31:25.734593: 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.
2017-04-16 20:31:25.735315: 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.

@George35mk I am so sorry, I thought you were having problems with OpKernel errors as I skimmed through your code at first and this issue was about that. These are just warnings saying TF can be faster in your machine if you build it from source. Setting TF_CPP_MIN_LOG_LEVEL to 2 is a proper solution, (rather 3) as it will silence WARNING logs. It is all working as expected. This messages will show every first time you run a TF program and will disappear afterwards if you don't set TF_CPP_MIN_LOG_LEVEL.

Hi Carmezin,
I have updated to tensorflow-1.1.0rc2-cp35-cp35m-win_amd64.whl and getting the following warning:
2017-04-19 13:28:04.456980: 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.
2017-04-19 13:28:04.457517: 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.
2017-04-19 13:28:04.457914: 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.
2017-04-19 13:28:04.458299: 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.
2017-04-19 13:28:04.458688: 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.
2017-04-19 13:28:04.459081: 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.
b'Hello, TensorFlow!'
b'1.1.0-rc2'

Here is the simple code:
import tensorflow as tf

if __name__ == '__main__':
    version = tf.constant(tf.__version__)
    hello = tf.constant('Hello, TensorFlow!')
    sess = tf.Session()
    print(sess.run(hello))
    print(sess.run(version))

Thank you for your help!
Ernest

ebonat this is the same like my error, just subscribe for future notifications if they solve this problem

Changing the TF_CPP_MIN_LOG_LEVEL to 2 will help? If so, how to do that? Where?
Thanks
Ernest

@ebonat @George35mk This is not an error but the expected behavior. As I already said above

These are not errors but just warnings saying TF can be faster in your machine if you build it from source. It is all working as expected now.
This messages will show every first time you run a TF program

TensorFlow binary was built without those advanced CPU instructions to be compatible with a wider range of machines by default. If you want to use TensorFlow with SIMD instructions, e.g. SSE, AVX etc, you need to compile TensorFlow from source.
TF_CPP_MIN_LOG_LEVEL is a TensorFlow environment variable responsible for its logs and works as follows:
If set to 1, it will filter out INFO logs, 2 it will additionally filter WARNING logs and 3 (not recommended) ERROR logs also.
One way to set its value is do this inside your TensorFlow script:
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf

Thank you and I hope this issue will be fixed soon!
Ernest

@ebonat It is not an issue hence there is nothing to be fixed. Please read again my answer.

This is not an error but expected behavior

OK, in this case: do i need to run this line of code os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" in all my python program using tensorflow?

If you want to silence the WARNING logs, it's an option. If you're in a Unix environment you can simply export TF_CPP_MIN_LOG_LEVEL=2 as well.
But again, it will only display the first time you run a program, after that it won't show up anymore.
A reference regarding SIMD instructions: http://stackoverflow.com/questions/43134753/tensorflow-wasnt-compiled-to-use-sse-etc-instructions-but-these-are-availab
@mrry do you know when #9279 will be updated to the website docs?

About: But again, it will only display the first time you run a program, after that it won't show up anymore.
This is very weird, I use Eclipse IDE/PyDev and I have to add this line at the beginning all the time.  If I run the program again without this line, the warning messages appear again. Why? Any idea?

AFAIK that is the expected behavior, perhaps is an Eclipse config thing but I don't know what can be in this case. Maybe @gunan has an insight on that.
You could set this variable globally as well so when Eclipse invokes the Python interpreter it's defined on your environment, on Windows I am not sure how would this be done, maybe using SET rather export.

I have found the solution. I created a new environment variable for the selected Python in PyDev settings. That's all.
Thank you for your help!
Ernest

make a video on youtube how to solve the ok , i want to see how you configure that environmental variable, thnx from george

ebonat i wait for your video the tensorflow community need you

hi george,
i'll do that!
Thanks
Ernest Bonat, Ph.D.
Senior Software Engineer
Senior Data Scientist

@mrry do you know when #9279 will be updated?

@Carmezim I thought it would be updated by now, but I'm honestly not sure how and when the website gets built these days. I'd guess it would be no later than the 1.1 release, but I'd hope it could be sooner....
@wolffg Do you know when the doc changes in #9279 (applied to the r1.1 branch) will be visible to website visitors? Should we backport them to the master or r1.0 branches as well?

@mrry I got it. I tried to help @George35mk up there also but he's still having problems apparently and I'd be just repeating myself, perhaps it wasn't clear but you might have a better way to clarify his doubts.
It'll be handy when the docs get updated due the usually time consuming help for this just becoming simply pointing the user to the docs.

Carmezim I understand you I run with "TF_CPP_MIN_LOG_LEVEL"] = "2" but is good to have no errors if the eboanat make video or some small tutorial how is set that ENV variable with that pydev is gonna be nice I am 2 weeks only with python and I don't know so much the python tricks

@Carmezim You've been extremely helpful, thanks! In terms of the log messages, I think we've been surprised by how surprised people by them (perhaps as a result of TensorFlow originally being designed as server software for Linux, where verbose logging is not so unusual).
We're trying to figure out a way to make these messages less worrisome and more actionable, but in the meantime having it in the FAQ will be useful, and that should hopefully happen soon!

@Carmezim, ye thank you very much for your help

@mrry I am also surprised myself for the response to the logs. Users familiarity with this kind of behavior has unexpectedly been oscillating a lot.
Thanks for the TF team efforts to make it a better experience for all :)

Yes, thanks to the TF team, so we can run TF in Windows too! I have found out already that the CPU release runs very slow. How can I get a good laptop (Windows or Mac) with a good GPU card and how much?

I am getting this error when running 'pip install tensorflow'
Command "python setup.py egg_info" failed with error code 1 in /private/var/folders/ys/zgxj5spx0k1gj8dl7rxq9n1c0000gn/T/pip-build-1hcjp_kb/protobuf/
Any idea what this is? Thanks!

Hey @Jarvis1985, this seems python/pip related and not a TensorFlow bug.

@Jarvis1985 Same issue here. Try running with as administrator under windows OS.

export TF_CPP_MIN_LOG_LEVEL=3 works for me but it always block all errors. Maybe those error messages should be print in level 2.


Problem authenticating to Blogger using Oauth2 and GoogleWebAuthorizationBroker.AuthorizeAsync

I've tried many things, tried to authenticate to Blogger using many samples from the web, tried with SharpDevelop, tried with Visual Studio 2015, tried with .NET 3.5, tried with .NET 4.5.2 and I get the same error every time coming from mscorlib "The directory name is invalid".

It looks like something from Google API, class "LocalServerCodeReceiver.cs:line line 86" is not going well. There is a Process start at the line which fails !!!! Why ????? I have the latest nuggets for Google.Apis (Google.Apis v1.20.0, Google.Apis.Auth v1.20.0, Google.Apis.Blogger.v3 <IP_ADDRESS>, Google.Apis.Core v1.20.0, Google.Apis.Oauth2.v2 v<IP_ADDRESS>2)
Please help me, I can provide more info if needed, even the solution and everything that will help me to use Google Api to insert and update Blogger posts from .NET.
The stacktrace is:
at System.Diagnostics.Process.StartWithShellExecuteEx(ProcessStartInfo startInfo)
at System.Diagnostics.Process.Start()
at System.Diagnostics.Process.Start(ProcessStartInfo startInfo)
at Google.Apis.Auth.OAuth2.LocalServerCodeReceiver.d__6.MoveNext() in C:\Apiary\v1.20\google-api-dotnet-client\Src\Support\GoogleApis.Auth.DotNet4\OAuth2\LocalServerCodeReceiver.cs:line 86
--- End of stack trace from previous location where exception was thrown ---
at System.Runtime.CompilerServices.TaskAwaiter.ThrowForNonSuccess(Task task)
at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task)
at Google.Apis.Auth.OAuth2.AuthorizationCodeInstalledApp.d__8.MoveNext() in C:\Apiary\v1.20\google-api-dotnet-client\Src\Support\GoogleApis.Auth\OAuth2\AuthorizationCodeInstalledApp.cs:line 77
--- End of stack trace from previous location where exception was thrown ---
at System.Runtime.CompilerServices.TaskAwaiter.ThrowForNonSuccess(Task task)
at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task)
at Google.Apis.Auth.OAuth2.GoogleWebAuthorizationBroker.d__4.MoveNext() in C:\Apiary\v1.20\google-api-dotnet-client\Src\Support\GoogleApis.Auth.DotNet4\OAuth2\GoogleWebAuthorizationBroker.cs:line 134
--- End of stack trace from previous location where exception was thrown ---
at System.Runtime.CompilerServices.TaskAwaiter.ThrowForNonSuccess(Task task)
at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task)
at Google.Apis.Auth.OAuth2.GoogleWebAuthorizationBroker.d__1.MoveNext() in C:\Apiary\v1.20\google-api-dotnet-client\Src\Support\GoogleApis.Auth.DotNet4\OAuth2\GoogleWebAuthorizationBroker.cs:line 60

I suspect the exception details would be useful here - you've shown us the stack trace, but not the exception, unless that's what you mean by "the directory name is invalid". (What's the exception type? Any more detail about which directory it's looking at?)

Try this https://github.com/LindaLawton/Google-Dotnet-Samples/blob/Genreated-samples1.0/Blogger API/v3/Oauth2Authentication.cs

jskeet, the exception message is in the attached picture. It's "The directory name is invalid". I don't know more about the invalid Directory  because it's a Google API internal exception. I hope someone to understand by looking in LocalServerCodeReceiver.cs:line line 86, where a Process.Start is called and fails !!!
LindaLawton, :( I get the same error, see the pic. I did nothing at Oauth2Authentication.cs class, just calling the method with my parameters.

Ok i will check it in the morning.

While digging a little I think I found the problem, but the fix will require a new release of Google API DLLs.
My error is thrown in Google.Apis.Auth.OAuth2.LocalServerCodeReceiver.cs line 82 when a Process.Start() is called and a "The directory name is invalid" error is generated. While looking at the LocalServerCodeReceiver.cs code, line 82 (https://github.com/google/google-api-dotnet-client/blob/master/Src/Support/GoogleApis.Auth.DotNet4/OAuth2/LocalServerCodeReceiver.cs), I found the method ReceiveCodeAsync() and the Process.Start(authorizationUrl); at line 86 which I think is generating the problem.
I'm wondering why the Google API tries to "Open a browser with authorizationUrl URL" at that line of code ? I don't know, but... opening a browser URL with Process.Start() could generate problems on some machines, as I found here: http://stackoverflow.com/questions/7693429/process-start-to-open-an-url-getting-an-exception
There are few options to fix this:
1 - make sure the authorizationUrl is well formatted and the Process.Start(URL) treats all machine-specific behaviours (make sure to catch Win32Exception and start the process using Process.Start("IExplore.exe", "http://myurl");).
2 - If the scope will let you, use a WebClient or another class to make a web request to that URL
Please let me know when a new release is available for Google.Apis.Auth.OAuth2 nuget

I cant recreate this.    I created two separate console applications  .net 4.6.1 and .net 4.0  both work fine i am not seeing your problem.   Note: We don't support .net 3.5 so its not going to work and we don't completely support  .net 4.0 any more either.  so make sure you are using at least .net 4.5
We need to prompt the user for authenticating using the auth uri.  Personally i think start process

Process.Start("IExplore.exe", "http://myurl");).

Is a bad idea considering that Mircorsoft is moving to a different browser and i for one would rather spawn the users default browser then force them to use a browser they don't prefer.
Can i get some information on your system and what user you are currently running as it almost sounds like you cant access System.Environment.SpecialFolder.Personal.

I'm using Windows 10 and .NET 4.5.
I was able to fix the problem and also reproduce the issue.
My problem was that my system wouldn't start the Default Browser when Process.Start("URL") is called because somehow Edge was deactivated. When I tried to manually set the default browser from Edge to Chrome in the Default Apps interface it didn't worked. I found a way to reinstall Edge and now the Google API DLL is working, Edge was showing and I was able to allow access to Blogger for my app.
I still think the Process.Start(URL) from LocalServerCodeReceiver should take into consideration if no (default) browser is present in the system and throw the appropiate exception.
A "try { Process.Start(URL); } catch { throw new Exception("Default browser could not be found" }" should do it.

I think the instance of a user not having a default browser selected is pretty low.    That being said I am all for notifying users of potential issues with nice messages.
You are welcome to submit a pull request if you want to fix it since your the one who found it :)

We definitely want to maintain the current behaviour of using the default browser.
As suggested, #957 throws a more helpful exception which should make problem-solving simpler.


Provide debugging tips in the docs.

This was something discussed in the meeting today. Perhaps a list of debugging tips may prove useful, but I'm not sure what to include, and how to formulate it i.e. perhaps an example using a specific waveform viewer?
Tips

Lower to vcd and use a waveform viewer to determine where things may be going wrong.
Use -vv when lowering with fud to determine which stage is leading to failure.
???

we should write this up more thoroughly, but here are a few:

compile to fud stage futil-noinline so that groups are still intact but you can find the FSM that corresponds to a group
compile to fud stage vcd, open in wave viewer, use FSM stage to orient where in the program execution you are
fud e <file> --to vcd_json | jq .TOP.main.signal | unique to quickly find if a signal ever changes

Started a debugging section in the docs. Let's update this when we think of new ways of debugging.

Is there a reason to keep this open? If https://github.com/cucapra/calyx/commit/89d801b8b68309e954d3bdce3eac112e9444da09 addresses the particular tips mentioned here, let's close this issue.


Big 162

Jira: BIG-162
KBC: https://github.com/keboola/connection/pull/4607
Before asking for review make sure that:
Checklist

[x] New client method(s) has tests
[x] Apiary file is updated

Release

[x] I gave the PR a proper label:

major (BC break)
minor (new feature)
patch (backwards compatible fix)
no release (just test changes)

Davam finalne na review, menil som len drobnosti. Mergnem toto do branche BIG-126-external-buckets a pak tam este na review ten PR


User reports that the VDC he deployed never appears

A test user has created a silver VDC and has paid for it.
See https://stellar.expert/explorer/public/account/GBBS7DOXWDZREDFBYFT5SVLMKIKPYZFDHCETZIK7S2JKHJJOJGQJN5ZR
In the end of the reservation process, an IP address was mentioned, but the overview of VDCs makes no mention of a to access the VDC.
IP reserved: <IP_ADDRESS>
3bot ID: sgherzi.3bot

| vdc_sgherzi_presearchnode0001_203ce7c5e5494c45805077dfe4e2981f        | owner: 2137      | IPWID:  33169     |

There were 2 deployments at the same time with the same name for the same user.

The first one at 21:46:02 with uuid 8372534a363d470ea45f3fea6f800c59

2021-04-15 21:46:02.632 | INFO     | jumpscale.sals.billing.billing:submit_payment:34 - payment dcf83c4c6c2b4aa8baffeece03e03a84 submitted for wallet: prepaid_wallet_8372534a363d470ea45f3fea6f800c59 amount: 14.999999999999996, description: {"type": "VDC_INIT", "owner": "sgherzi", "solution_uuid": "8372534a363d470ea45f3fea6f800c59"}, expiry: 5, memo_text: flvnpc9zmGM0eWPPKcHWohp93yjE

The second at 21:47:13 start to deploy another one with uuid 203ce7c5e5494c45805077dfe4e2981f

2021-04-15 21:47:13.476 | INFO     | jumpscale.sals.billing.billing:submit_payment:34 - payment f9ac45e271d44e83827e314b46e0a290 submitted for wallet: prepaid_wallet_203ce7c5e5494c45805077dfe4e2981f amount: 14.999999999999996, description: {"type": "VDC_INIT", "owner": "sgherzi", "solution_uuid": "203ce7c5e5494c45805077dfe4e2981f"}, expiry: 5, memo_text: 4KDYhFCDX9r0dYl06Ftvj1fFsutm

The first deployment instance was deleted when the second one started as the first one was empty.
The first deployment keep waiting for payment, till it timed out at 21:51:06 (after 5 mins)
The first deployment deleted the second one's instance at 21:51:07, as the payment timed out.
The second deployment got payment at 21:51:19.
The second deployment continued the deployment with the instance in memory.
And when it's done the instance wasn't there.

Now there is locking for VDC deployment, so a VDC with the same name can't be deployed at the same time for the same user.
Also, the VDC instance presearchnode0001 has been restored.

Comment from user:
Hi,
The node is still down. When I click on "Deployed Solutions" I get sent to a page that is stuck on "Loading Items". Also, when I click "Go to My VDC" it's just a white page that is stuck trying to load at this point.

There was an issue access the kubernetes cluster through the public and it should be resolved now.
https://github.com/threefoldtech/zos/issues/1249


Quick Open should be able to find the file item if the path entered is a full existing relative path to the project root, even a "search.exclude" settings excluded the path

Corresponding to this ticket: #15604
and this comment: https://github.com/microsoft/vscode/issues/15604#issuecomment-582788918

Or furthermore to a new command, may the default Quick Open action able to show the file item if the path entered is a full existing relative path to the project root?
I feel it quite strange & stupid, to discover that "Quick Open" cannot find the file I want, even I gave the full relative path, which is the almost-complete hint. :-/ (Always needing to copy from or make an absolute path is tedious)

Reasoning
Use case example: For some PHP exception tracing from apache server's log, I need to open some files locally in the "vendor" folder (excluded from search.exclude setting).
"search.exclude" is majorly for Ctrl-Shift-F (Text search) and only semi for Ctrl-P (Quick Open). Although not showing the excluded files normally in Ctrl-P, it is good to make Quick Open easier to open those files with a full relative path hint. (to be more user friendly)
Remark: Fire this request because it can be considered separately from #15604. Although either will help.

We actually had this feature before, and we removed it due to complicating things and low use. You can actually do this with an absolute path though, if that helps.

I see. BTW, the server's log absolute path for the uploaded file is different with local path, so absolute path doesn't help in this case.
However, closing this is acceptable, as this may complicate VSCode's code... XD (but may be more user friendly?). Thank you.
It still have a workaround to do a command "code relative-path", although more typing. :)


configuration option to specify the spectrum (tcp) server address

When rtlamr runs within the container, it looks for the tcp server at <IP_ADDRESS>:1234. Would it be possible to add a configuration option in the yaml to allow setting a custom address? I have a non-Dockerized instance of rtl_tcp but this is the perfect bridge to Home Assistant so I hope I can get them to talk to each other. Thanks!

Code merged. Thank you @jonbloom


Input Frames

We're currently processing your upload.  This comment will be updated when the results are available.


[Enhancement] Replace Address Field with Email Field and Capitalise all values stored in student fields

Main changes:

Remove Address Field
Add Email Field to store a student's parent email address
Following fields should all be capitalised: (Users need not capitalise their input, these values are capitalised when stored)

Name
Id
Class
Email
Parent Name


updating xcconfig to match MSAL podspecs

Proposed changes
Updating development SDK's to match our POD specs for MSAL.
Type of change

[ ] Feature work
[ ] Bug fix
[ ] Documentation
[ x ] Engineering change
[ ] Test
[ ] Logging/Telemetry

Risk

[ ] High – Errors could cause MAJOR regression of many scenarios. (Example: new large features or high level infrastructure changes)
[ x ] Medium – Errors could cause regression of 1 or more scenarios. (Example: somewhat complex bug fixes, small new features)
[ ] Small – No issues are expected. (Example: Very small bug fixes, string changes, or configuration settings changes)

Additional information

@aherciya, could you either merge or close this one?


Allow managing multiple BIP44 accounts for a device

Currently keeper is only designed to work with the 0 account of a signing device
Yet it is important that we allow users to add keys that belong to the same signer but from different BIP44 accounts, and let the user choose which account they wish to use when creating a wallet/ vault in case they have multiple accounts added.
We will need to first design the UI/ UX for this behavior.

Have made the design for this, please check to see if its according to requirements @ben-kaufman
Section:
https://www.figma.com/design/m5eMFTKD391AiJAXaEzgxB/Bitcoin-Keeper?node-id=5867-11978&t=bx02hhg8L23M6G58-1
S.S

Have done the edits after feedback now. On Additional Info screen , we'll show the account number on top right instead of showing in input feild.
Also One Signer can have different accounts, those will be displayed in this section here with “(1),(2) etc” that too in the logic area
Logic area: display description if available otherwise just the signer addition date. Now in both cases the (1), 2 etc will be added if another account from same signer has been added.
Section Link:
https://www.figma.com/design/m5eMFTKD391AiJAXaEzgxB/Bitcoin-Keeper?node-id=5867-11978&t=NnBd7Am3YSgyRulf-1
S.S

#5252 - xpub issue

Already completed in 41


Add Multiple instances support

Hi @pcfens,
We ship most of our logs to Logstash for parsing and enrichment.
As some of our logs are already formatted as JSON objects and do not need any modification, we can ship those lines directly to ES.
The dependencies for this feature are:

Install FileBeat 5.x (which introduced JSON parsing feature, among other features)
Support installing multiple instances of FileBeat on the same host.

The later is due because FileBeat itself doesn't have the ability to configure which prospector ships through which output.
You're more then welcome to take a look on this fork. the above bullets are implemented and working.
I'd be happy to get your take on the above :)
N.B.
I still need to modify the tests to the new structure and solve some Docker issues preventing Beaker tests from running.
Thank you,
Yarden

I just released v5 support (though without JSON parsing settings - soon though, I'll probably grab code from your fork for that).
I hadn't thought of running multiple instances on a single host, but it makes sense.  If we can add support for non-systemd systems to your fork then I think that model will work really well. I'm also trying to maintain puppet 3 compatibility for a little bit longer, hopefully at least until its official EoL at the end of the year.

Hi @pcfens,
What are the next steps? how can we move this forward?

We need to get init/upstart/systemd templates set up, ideally with beaker tests. I like the approach you've used in your fork, we just need to expand it a bit.
I won't have the time to write it in the next few weeks, but am happy to help.

+1

+1 we have multiple outputs that we don't want delayed if one is down.

+1 for this. We also have some inputs which need to go through logstash and some who we'd like to just sent directly.

It would be awesome to add multiple instances. Is there any news on this ?

I think that it's a great idea - I just haven't had any time to work on it. I'm always open to PRs if someone else has time.

+1 for multiple instances, we need it to send items to different logstash servers (customer+we) and we don't want to install a local logstash for routing.

+1 for this

+1

Definitely +1
elastic-elasticsearch supports multiple instances. Maybe some inspiration could be taken from there :)
https://github.com/elastic/puppet-elasticsearch/blob/master/manifests/instance.pp


Device configuration lost if device is not connected

If I disconnect the device I configured, the Events do not appear on Editor Restart.
With Connected Device and Configuration set:

But if I disconnect the device, this happens:

With warning being:
"InputAxisKey Event specifies invalid FKey'Joystick_TFlightHotasX_0_Axis0' for  Joystick_TFlightHotasX_0_Axis0"
And if I search any input Event, they do not come up
.
It seems the events depend on whether there's a configuration AND if the device is connected, but we don't want to be dependant on the device being connected or not. Plus, we have an array of Joysticks to use and configure and we can't have all of them connected at the same time.
I tried to replicate with two joysticks at the same time and the same happens: while both configured and connected, they work, but if I disconnect one of them, its events disappear

An update: turns out if I compile with the device connected, the event shows up again, which won't be a problem with Inputting on Input Actions. But for some reason I cannot seem to map any axis, only buttons

Okay, second and hopeful final update:
On my project, we have two possible pawns to control through two different player controllers and on the same device. In order to handle this, we had to override the Game Viewport Client and handle input to the correct pawn there using an overridable InputKey function.
What I noticed here with the plugin was that the Keys from the Joystick were being recognized by the First Pawn as well as the Second Pawn, but the Axis were only on the First pawn. This made me realize I needed to override the InputAxis function on the Game Viewport Client similar to the InputKey function in order to get them working and alas they do!
So finally the project is handling the input from the joystick correctly :D


Automate deployment to S3

This can be done with Travis. See this example.

It remains to give this AWS auth pair access to write to the bucket.

Huh. I gave it access (I think), but the build still fails. There's more debugging to be done here.

Needed to add a couple more user policies (the ability to write to a bucket, instead of just its contents, and the ability to list buckets). That fixed it.


fix: count column aliases only once

Alias column names don't exist in the table definition. When we take it from the select target list, we only need to do this once (and not once for every table in a join statement).
Refs: #2398 #1886

Thanks for this and sorry for the delay. There is definitely a bug, but I don't know if restricting the restarget counting is the right fix (although your change does seem to have "fixed" the bug). The issue seems to be that sqlc doesn't understand what list of refs the aliases in ORDER BY and GROUP BY clauses should be checked against.
I'm going to dig in a little bit and see if I can come up with a recommendation for a different approach.


Clear prefixMatch array every time a new request is sent to the server.

At the moment, prefixMatches array keeps track of any pre-existing matches.

This has to do with the empty endpoint which returns the first five words in the dictionary.


Failed building wheel for pyaudio

<<Please describe the issue in detail here, and for bug reports fill in the fields below.>>
Reproducing code example:
<<A short code example that reproduces the problem. It should be self-contained, i.e., possible to run as-is via 'python myproblem.py'>>

Error message:
<<Full error message, if any (starting from line Traceback: ...)>>

Scipy/Numpy/Python version information:
<<Output from 'import sys, scipy, numpy; print(scipy.__version__, numpy.__version__, sys.version_info)'>>

There is not enough detail to determine whether this is a SciPy problem or to point you in the right direction if it isn't.


Randy

Some files didn't commit for some reason, extra pull request cause of that.

Just to point out, since the initial pull request, I've added a few things like extremely basic enemy ai, sound effects, extra art, and saving a level as my own level so we have less conflicts with overwriting levels when syncing and testing our own stuff. That's it... Oh, and here's a video showing the updates... perhaps I should comment the video link into all of the pull requests I do.
https://www.youtube.com/watch?v=Rd691KnCnuQ


[Documentation] Clarify how containers are matched with names

I spent quite some time before understanding how names are mapped to container, the documentation says :
'Dnsdock connects to Docker Remote API and keeps an up to date list of running containers. If a DNS request matches some of the containers their local IP addresses are returned.'
Is the name of the container matched to the DNS query ? That sounded like a reasonable assumption to me when reading the docs but it's actually not the case!
It took me some time to discover that the name of the IMAGE used to create the container was matched in priority.
if you have 2 containers (named titi and tata) started from the same image "alpine:3.2" for example
dig titi.docker -> no answer section
dig alpine.docker -> addresses of both titi and tata in the answer section (in random order)
dig titi.alpine.docker -> address of titi
dig tata.alpine.docker -> address of tata

nevermind it's actually my fault for not readin the doc well enough !


Invoke #event_monitor_running once the event catcher has started

This change will prevent event catchers from hanging.
Fixes #5347

@matthewd, @Fryguy & @blomquisg Please review

I'm ok with merging this for now, but I don't understand the reasoning behind the event_monitor_running needing to be placed inside the each_batch in some cases, but it's ok outside the loop in other cases.  If it's consistent outside the loop, this whole pattern can be refactored out to start_monitor_events directly, and then a provider plugin writer doesn't have to know about it at all.

After reading #4939, I'm wondering if we have another possible issue.  If the monitoring thread goes into a bad state, then will the main thread be locked preventing it from heartbeating?

@Fryguy only if it happens during startup, and "bad state" == "hang" (not exception). But that sounds like a pretty good reason for the server to kill us, to me.
Agreed on refactoring to reduce knowledge in the subclasses; we just wanted to get it fixed for now.


Add ENV_NAME as param for deployment

Enables us to deploy hac-core to the ephemeral env with the correct ephemeral environment configured.

/lgtm

/approved

/approve

/approve


The VTC conference id.

what is the formate of VTC id? for example?

@poxiaosyb Thank you for submitting feedback and contributing to the docs. We are currently investigating this.

@officedocsbot assign @scanum

@poxiaosyb thank you for submitting feedback.
I think the best way forward is if you open a service ticket in your tenant so this can get resolved ASAP. Based on the outcome let me know if it is something that can be called out in the docs.
Please follow this link to contact support for business products:https://docs.microsoft.com/office365/admin/contact-support-for-business-products
Please keep us posted here on the resolution so we can feed whatever information you discover into the content.
@officedocsbot close

@officedocsbot close


Fix typo in docstring

Codecov Report

Merging #173 (e0d1e48) into master (ecd613d) will not change coverage.
The diff coverage is n/a.

@@            Coverage Diff             @@
##            master      #173    +/-   ##
==========================================
  Coverage   100.00%   100.00%
==========================================
  Files          143       143
  Lines        12392     12068   -324
==========================================
- Hits         12392     12068   -324

Impacted Files
Coverage Δ

ormar/queryset/queryset.py
100.00% <ø> (ø)

tests/test_types.py
100.00% <0.00%> (ø)

ormar/queryset/clause.py
100.00% <0.00%> (ø)

ormar/models/excludable.py
100.00% <0.00%> (ø)

ormar/fields/foreign_key.py
100.00% <0.00%> (ø)

tests/test_signals/test_signals.py
100.00% <0.00%> (ø)

tests/test_queries/test_order_by.py
100.00% <0.00%> (ø)

tests/test_queries/test_or_filters.py
100.00% <0.00%> (ø)

tests/test_queries/test_pagination.py
100.00% <0.00%> (ø)

tests/test_relations/test_cascades.py
100.00% <0.00%> (ø)

... and 66 more

@collerek Somewhat related, why does all() return List[Optional["T"]] when the docs say:
If there are no rows meeting the criteria an empty list is returned.

Shouldn't that indicate that it returns List["T"] and it will just be an empty [] if it doesn't find anything? What does None communicate?
The Optional seems to flow form:
    def _process_query_result_rows(self, rows: List) -> List[Optional["T"]]:
        """
        Process database rows and initialize ormar Model from each of the rows.

        :param rows: list of database rows from query result
        :type rows: List[sqlalchemy.engine.result.RowProxy]
        :return: list of models
        :rtype: List[Model]
        """
        result_rows = [
            self.model.from_row(
                row=row,
                select_related=self._select_related,
                excludable=self._excludable,
                source_model=self.model,
                proxy_source_model=self.proxy_source_model,
            )
            for row in rows
        ]
        if result_rows:
            return self.model.merge_instances_list(result_rows)  # type: ignore
        return cast(List[Optional["T"]], result_rows)

As for Optional you are right, not sure why it's there maybe it's coming from old code or maybe it was always a bug. If you can, please remove optional and make sure ormar checks with mypy (so whole method chain needs to be adjusted probably).

Come to think about it i think i forgot to update the do docstrings for all queryset and queryset proxy methods, i just updated the docs. All of them, if accept kwargs and args, and refer to filter in docstrings should have filter(*args, **kwargs)

As for Optional you are right, not sure why it's there maybe it's coming from old code or maybe it was always a bug. If you can, please remove optional and make sure ormar checks with mypy (so whole method chain needs to be adjusted probably).

I broke this into its own issue, #173, so we don't forget about it as I can't get to it today.

Come to think about it i think i forgot to update the docstrings for all queryset and queryset proxy methods, i just updated the docs 🙈. All of them, if accept kwargs and args, and refer to filter in docstrings should have filter(*args, **kwargs)

@collerek Based on this I'm not sure what needs to be changed in this PR. Should it be closed?
Or did you mean the change should be:
Passing kwargs is a shortcut and equals to calling `filter(*args, **kwargs).all()`.

Or did you mean the change should be:
Passing kwargs is a shortcut and equals to calling `filter(*args, **kwargs).all()`.

The second one, so:
# Passing args and/or kwargs is a shortcut and equals to calling `filter(*args, **kwargs).all()`.

And the same applies for other methods: get, get_or_none, get_or_create etc.
All methods from QuerySet that now accepts also *args and has this reference to filter() in docstring should be updated like the line above. The same applies to equivalent methods located in Querysetproxy.
If you can update them all I would be grateful, if not I will do this in next release.


Update thiserror

I forgot to do this earlier when we investigated the issue.

Codecov Report

Merging #351 into master will not change coverage by %.
The diff coverage is n/a.

@@           Coverage Diff           @@
##           master     #351   +/-   ##
=======================================
  Coverage   79.81%   79.81%
=======================================
  Files          26       26
  Lines        6883     6883
=======================================
  Hits         5494     5494
  Misses       1389     1389

Continue to review full report at Codecov.

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⚠ GitHub has degraded performance

In 6178ed3, GitHub (https://www.githubstatus.com/api/v2/status.json) experienced degraded performance:

HTTP code: 200
Response time: 159 ms

Resolved: GitHub performance has improved in 7ca88df after 15 minutes.


attester: code refactoring

Signed-off-by: Denis<EMAIL_ADDRESS>

@macpijan @arturkow2000 Please review this PR.

@arturkow2000 fixed. please review


Error not propagated to user

Not all errors are transferred from the server to the user making it difficult to understand what has gone wrong.

Do you have an example I can use to start investigating this?


Feat: User sessions with open ai api key

save logins, scripts and workspace based on the openai api key

The OpenAI key is already persisted in localstorage. Additionally, we have an issue here for saving scripts to localstorage for subsequent visits (https://github.com/polywrap/evo.ninja/issues/71).
So, to call this task complete, we'd need to persist (1) chat history & (2) workspace files.


Feature request : Add a copy to clipboard button next to links

More often I need to copy the links and share it with someone. Right now I have to right click on "Link" and copy the address.
If possible it would be nice to have a one click copy button.

Hi.
This is now implemented in version v4.2.
https://github.com/paveyry/better-hangoutschat/releases/tag/v4.2

Readded this feature in v6.1.1


Issue with running locally

>>> from personal_graph.graph import GraphDB, OllamaClient, OllamaEmbeddingClient
from personal_graph.graph_generator import OpenAITextToGraphParser
from personal_graph.database import SQLite
from personal_graph.vector_store import VliteVSS

phi3 = OllamaClient(model_name="phi3")
nomic_embed = OllamaEmbeddingClient(model_name="nomic-embed-text")

storage_db = SQLite(local_path="./local.db", vector0_so_path="/path/to/vector0/extension", vss0_so_path="/path/to/vss0/extension")
vector_store = VliteVSS(collection="./vectors")

graph_generator=OpenAITextToGraphParser(llm_client=phi3)
print(graph_generator) # Should print the InstructorGraphGenerator

with GraphDB(
    database=storage_db,
    vector_store=vector_store,
    graph_generator=graph_generator
  ) as db:
    print(db)

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ImportError: cannot import name 'OllamaClient' from 'personal_graph.graph' (/Users/satyamtiwary/Documents/Pytorch-Things/personal-graph/personal_graph/graph.py)
>>> from personal_graph.graph_generator import OpenAITextToGraphParser
>>> from personal_graph.database import SQLite
>>> from personal_graph.vector_store import VliteVSS
>>>
>>> phi3 = OllamaClient(model_name="phi3")
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'OllamaClient' is not defined
>>> nomic_embed = OllamaEmbeddingClient(model_name="nomic-embed-text")
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'OllamaEmbeddingClient' is not defined
>>>
>>> storage_db = SQLite(local_path="./local.db", vector0_so_path="/path/to/vector0/extension", vss0_so_path="/path/to/vss0/extension")
>>> vector_store = VliteVSS(collection="./vectors")
>>>
>>> graph_generator=OpenAITextToGraphParser(llm_client=phi3)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'phi3' is not defined
>>> print(graph_generator) # Should print the InstructorGraphGenerator
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'graph_generator' is not defined
>>>
>>> with GraphDB(
...     database=storage_db,
...     vector_store=vector_store,
...     graph_generator=graph_generator
...   ) as db:
...     print(db)
...
Traceback (most recent call last):
  File "<stdin>", line 4, in <module>
NameError: name 'graph_generator' is not defined

Also would need to update storage_db = SQLite(local_path="./local.db", vector0_so_path="/path/to/vector0/extension", vss0_so_path="/path/to/vss0/extension") to storage_db = SQLite(local_path="./local.db") in the README