Скачать или смотреть errors and issue faced installing tensorflow GPU and yolo darknet GPU

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   / @robinroypeter__  




Placeholders are objects that allow you to feed in
data of a specific type and shape and depend on the results of the computational graph, such
as the expected outcome of a computation.


if you have errors installing the extra depandency and have some clash so please use time shift or restore the system back to the date where your tensorflow seems to be working fine

my environment variables i used are below which does not result in any hardlink soft link clash and also clash between dependency but installing tensorflow inside a virtual environment will be a good practice
export PATH="/usr/local/cuda-9.0/bin:$PATH"
export LD_LIBRARY_PATH="/usr/local/cuda-9.0/lib64:LD_LIBRARY_PATH"

export PKG_CONFIG_PATH=/usr/local/lib/pkgconfig:$PKG_CONFIG_PATH


export CPATH="/usr/include/hdf5/serial/"









https://stackoverflow.com/questions/6...

if matplotlib module not found in jupyter notebook follow instruction from this site
https://stackoverflow.com/questions/1...
better to install tensorflow -1.8.0 cuda -9 and cudnn-7.0.5

import tensorflow as tf
tf.__version__
'1.8.0'


if you guys have a problem in installing tensorflow in ubantu 18.04 bionic beaver and linux mint -19

follow this tutorial below where you can install tensorflow without error

is it easier to setup tensorflow on ubuntu compared to windows? i am getting so many errors, usually related to the path variables on windows.

  / setup-a-simple-environment-for-deep-learning  

for error free tensorflow tutorial please watch this tutorial below

  / setup-a-simple-environment-for-deep-learning  

https://askubuntu.com/questions/97807...

to test tensorflow installed for GPU use these below code or the details in the website

import tensorflow as tfwith tf.device('/gpu:0'):a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')c = tf.matmul(a, b)sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))# Runs the op.print(sess.run(c))
  / using-tensorflow-gpu-on-a-compute-3-0-grap...