Defining, creating, using and maintaining a development environment
If you're new to machine learning, you may have a development environment you can access through your school or your workplace. If you're on your own, you can take an online course (see below) that can point you in the direction. Barring that, below are some tips to get you started.
Tensorflow 2.0 with Keras (TF2) has been popular for deploying production-level machine learning applications, and PyTorch has been the favorite in classroooms. But PyTorch has been gaining ground on TensorFlow, which to use, and do you have to choose?
Firstly, you can't "mix" in TF2 to PyTorch programs because neither framework is purely python code, and they each have their own compiled C++ backends. Rather, pick one framework or the other for an application, to avoid having problematic conflicts in the CUDA environment.
There are so many ways to do this its a bit overwhelming. Here's a simple way to get great looking documentation quickly.
Attempting to push a large file to github via `git push` may trigger an error similar to the following:
A software environment consists of tools and data. Tools may include software such as:
The data includes everything accessible by the software on disk, in memory, and accessible over the network, such as:
