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How to Install TensorFlow on Debian 9


How to Install TensorFlow on Debian 9

TensorFlow is a free and open-source stage for AI worked by Google. It is utilized by various associations including Twitter, PayPal, Intel, Lenovo, and Airbus. 

TensorFlow can be introduced framework wide, in a Python virtual climate, as a Docker holder or with Anaconda. For learning purposes, it is ideal to introduce TensorFlow in a Python virtual climate. This way you can have various diverse disengaged Python conditions on a solitary PC and introduce a particular adaptation of a module on a for each task premise without stressing that it will influence your different Projects. 

This instructional exercise will manage you through the way toward introducing TensorFlow on Debian 9. 

Installing TensorFlow on Debian 9

The accompanying areas give a bit by bit directions about how to introduce TensorFlow in a Python virtual climate on Debian 9. 

1. Installing Python 3 and venv

Of course, Debian 9 boats with Python 3.5. To confirm that Python 3 is introduced on your framework type: 

python3 -V

The yield should resemble this: 

Python 3.5.3

In the event that you need to utilize Python 3.7 all things being equal, check these guide. 

The prescribed method to establish a virtual climate is by utilizing the venv module. Introduce the python3-venv bundle that gives the venv module by running the accompanying order: 

sudo apt install python3-venv

When done we can continue with the subsequent stage and establish a virtual climate for our TensorFlow task. 

2. Creating a Virtual Environment

Explore to the index where you'd prefer to store your Python 3 virtual conditions. It tends to be your home index or whatever other registry where your client has peruse and compose authorizations. 

Make another catalog for the TensorFlow undertaking and disc into it: 

mkdir my_tensorflow
cd my_tensorflow

From inside the index, run the accompanying order to establish the virtual climate: 

python3 -m venv venv

The order above will make a catalog named venv, which contains a duplicate of the Python double, the Pip bundle supervisor , the standard Python library and other supporting records. Utilize any name you like for the virtual climate. 

To begin utilizing the virtual climate, you'll have to enact it by running the actuate content: 

source venv/bin/activate

When actuated, the virtual climate's receptacle catalog will be added toward the start of the $PATH variable . Likewise the shell's brief will change and it will show the name of the virtual climate you're presently in. For this situation that is venv. 

TensorFlow establishment requires pip variant 19 or higher. Run the accompanying order to overhaul pip to the most recent variant: 

pip install --upgrade pip

3. Installing TensorFlow

Since we've established a virtual climate, the subsequent stage is to introduce the TensorFlow bundle. 

pip install --upgrade tensorflow

On the off chance that you have a committed NVIDIA GPU and need to exploit its handling power, rather than tensorflow introduce the tensorflow-gpu bundle which incorporates GPU uphold. 

Inside the virtual climate, you can utilize the order pip rather than pip3 and python rather than python3. 

When the establishment is finished, confirm it with the accompanying order which will print the TensorFlow variant: 

python -c 'import tensorflow as tf; print(tf.__version__)'

At the hour of composing this article, the most recent stable rendition of TensorFlow is 2.0.0 


Your TensorFlow adaptation might be unique in relation to the variant appeared previously. 

In the event that you are new to TensorFlow, visit the Get Started with TensorFlow page and figure out how to assemble your first ML application. You can likewise clone the TensorFlow Models or TensorFlow-Examples storehouses from Github and investigate and test the TensorFlow models. 

At the point when you are finished with your work, type deactivate to deactivate the climate and re-visitation of your typical shell. 



In this instructional exercise, we have told you the best way to introduce TensorFlow on Debian 9. 

On the off chance that you hit an issue or have criticism, leave a remark underneath.