I am a new user to conda environment and was setting up to use TensorFlow , on Windows. I came across a command - source activate IntroToTensorFlow.
I understood IntroToTensorFlow is an environment we are creating, but does it mean we need to create this environment every time?. I am using jupyter notebook, so if I shutdown the kernel will the environment get deactivated? And if I restart my PC, should I activate the environment everytime ?
source activate IntroToTensorFlow does not create an environment, it simply activates an environment that has already been created. To create that environment (with tensorflow installed), use conda create -n IntroToTensorFlow tensorflow.
You do not need to create the environment every time, but you do need to activate it every time in order to use the packages installed in it. This is done using source activate IntroToTensorFlow
If you shutdown a kernel, the environment does not get deactivated automatically. To do so, you have to explicitly say source deactivate, or activate a separate environment using source activate xxx, replacing xxx with whatever environment name you want (that you have created previously).
When restarting your PC, (or starting a new session at the command line), you have to manually activate your desired environment to use it. Otherwise, by default, it will be running in your root environment. So, if you've only installed tensorflow in IntroToTensorFlow environment, you have to use source activate IntroToTensorFlow every time in order to use it.
Take a look here for more info
Conda is a package manager that installs and manages (usually) Python libraries and (sometimes) non-Python packages. A conda environment is a sort of virtualenv virtual environment; its typical use case is to have a Python interpreter (any version) along with your choice of compatible Python libraries (any version).
The following example might most likely pertain to you. Suppose you have downloaded the implementation of a very nice paper implemented in TF and you want to try it out. But the authors implemented that when Tensorflow was just growing. The APIs have changed now, and so is the required CUDA version. You want to work ideally on the latest TF. Now, what do you do? An easy way to just try out this implementation is to create a different conda environment with the libraries needed for that implementation, run that in this environment, and perhaps if you like it, you might consider upgrading the TF APIs and use it in your code.
The conda environments are also pretty simple in its construction. If you installed conda using Anaconda and default options, you will have your environments in ~/anaconda3/envs. The environments are nothing but directories here, each having various configurations of Python interpreter and libraries of your choice. (So when you shutdown your PC/Jupyter, the environments will of course persist.) At the time of usage, you just switch between the environments to suit your needs. That is, when you source activate an environment, you will be allowed to use the Python interpreter and installed libraries from that environment. Note if you source deactivate or start a new terminal session, you will still be using the root environment.
Besides, Jupyter notebook, if setup with this plugin, will allow you to have nice integration with conda environments and you wouldn't even need to source activate everytime you want to switch. You can choose between the various settings (or conda environments), which are interpreted as different kernels in the notebook. So it would as simple as choosing some environment using a drop-down.
