It installs the required set of libraries to start with Tensorflow. # Creating tensorflow virtual environment (base) C:\Users\Aditya\> conda create -n tf tensorflow #Activate the environment (base) C:\Users\Aditya\> conda activate tf Now that we’ve done CRUD with the virtual environments, for tensorflow user can create a virtual environment using following set of commands below. To delete the environment using: (base) C:\Users\Aditya> conda env remove -n myEnvironment Setting up TensorFlow Environment using Anaconda You can exit the environment using the command conda deactivate (myEnvironment) C:\Users\Aditya> conda deactivate (base) C:\Users\Aditya> conda activate myEnvironment (myEnvironment) C:\Users\Aditya> Post the command, user can see the base changing to indicating change of environment.
The environment can be activated using the following command : conda activate. To create a virtual environment the command is as below: (base) C:\Users\Aditya> conda create -n myEnvironment It prevents issues/errors in code execution related to deprecation of commands or release of newer versions. To provide a brief overview, a virtual environment simply provides a separate environment where you can do application development using specified version of libraries. I pursued using anaconda prompt ( or anaconda CLI) given my recent obsession with working on black and white windows.
Using Anaconda Prompt - the command line tool.īoth of the approaches have their own pros and cons.Using the User Interface - Anaconda Navigator.The choice is totally up to the user depending on their comforts with the command line or with the GUI. There are two ways in which a user can go about adding libraries and performing other operations in anaconda. Setting up TensorFlow with Jupyter Notebooks has been one of the issues which I faced when trying to get it to work so I’m putting together a series of steps just as FYI ( in case my memory goes bust someday ). TensorFlow has become one of the most used frameworks in the deep learning space providing ability to train models in python providing encapsulation of the boilerplate code and aiding in deployment of applications on cloud.
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