You can create an environment with the Python version of your choice.
Example create an environment called deep with python 3.5 and tensorflow:
conda create -n deep python=3.5 tensorflow
After that we can activate it with
conda activate deep
While in this environment you will have Python 3.5 and tensorflow. You can add other packages to your environment anywhere. Eg adding latest scipy, pandas, and jupyter
conda install --name deep scipy pandas jupyter
Updated: While in the environment, you don't have to specify the environment name, when installing packages. You can do:
conda install package_name
When done doing awesomeness, you can deactivate as so:
conda deactivate
;) So your workflow when working with Tensorflow, would include activating your 'deep' environment and use Python 3.5 there ;) eg
conda activate deep
jupyter lab
Assuming you have installed tensorflow and jupyter, this will start a service on your default browser where you can start building your project.
Happy coding ...
Check out conda documentation https://conda.io/docs/user-guide/tasks/manage-pkgs.html
Today Tensorflow doesn't support Python 3.7 . You have to create a new environment with Python 3.4, 3.5 or 3.6. With conda
it's easy to handle different environments and versions. In addition it's recommended using pip
to install Tensorflow.
Python 3.6 with CPU:
conda create -y -n name_of_env python=3.6 # create new environment
source activate name_of_env # activate the new environment
pip install tensorflow # install tensorflow
Python 3.6 with GPU ( please check the additional setup for using a GPU ):
conda create -y -n name_of_env python=3.6
source activate name_of_env
pip install tensorflow-gpu
Tip: Finally you can test your installation with the following command:
echo 'import tensorflow as tf; print(tf.__version__)' | python
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