Setup PyCharm for Deep learning with TensorFlow, Keras and Jupyter (with virtualenv)

This is a startup post to get your dev environment setup for diving into Deep Learning. I have chosen to begin with TensorFlow and Keras for this task. I would be using the Jupyter notebook for demonstrating the dev setup.

Note: Jupyter itself is self sufficient for this task,
but I am using PyCharm for this task just to test the interoperability of PyCharm 
with other ecosystem stack.

Create a new project with virtual env

Create a new Pure Python project in PyCharm and provide the settings for a virtual env. Pre-requisites for this step are python and virtual env. Make sure you have these steps before creation of new project:

  • Install Python
  • Install pip
  • Install virtualenv (pip install virtualenv)

PyCharm settings for new project. Make sure you chose python 3.6 for the virtual env :

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Activate the virtual env if not already activated:

  • Open the terminal from PyCharm
  • Activate virtualenv :
$> source venv/bin/activate

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Terminal command:

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Install all python requirements for our project

Create a requirements.txt file in the project and add the python dependencies in it.


It should prompt you to install the dependencies, click on Install requirements. Or alternatively you could use the command line to install all the requirements:

pip install -r requirements.txt

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Packages installed:

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Create a Jupyter notebook

Jupyter notebook can be created via PyCharm or directly on the console.

From PyCharm

  • Create a Jupyter notebook
  • Click on the Play button to connect to Jupyter
  • Hit cancel, to create local run configurations
  • Click on the notification saying – Run Jupyter notebook
  • Optional – open the Jupyter link in browser


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Directly from console if you only want to use Jupyter:

Hit this command on the console:

jupyter notebook

Then visit the browser with the printer URL on console.

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Test some sample code

The sample code to test here has been picked from : tensorflow-visualization.

Use the below code on the browser or PyCharm to visualize a graph.



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Thats all for this quick post. Hope its helpful.


Extra (troubleshooting):

TENSOR Installation Error:

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