I have been spoilt working commercially in the JavaScript world by the npm package manager and it's related eco-system. When I have been working with python, there are numerous frustrations related to versioning and working with third party packages. It is these third party packages that have made Python the programming language for machine learning.

Why? #

Anaconda is an open source toolkit for:

• Dependancy management - provides a cloud-based repository of over 7,500 data science and machine learning packages.
• Manage Environments - makes it easy to manage multiple environments that can be maintained and run separately without interference from each other.

Why can't I just use pip, easy_install and virtualenv/pyenv? #

Good question! The simple answer is that Conda support dependancies that sit outside the Python ecosystem. This Conda blog post addresses this question:

Having been involved in the python world for so long, we are all aware of pip, easy_install, and virtualenv, but these tools did not meet all of our specific requirements. The main problem is that they are focused around Python, neglecting non-Python library dependencies, such as HDF5, MKL, LLVM, etc., which do not have a setup.py in their source code and also do not install files into Python’s site-packages directory.

What are these libraries that get mentioned?

• HDF5: HDF5 is a unique technology suite that makes possible the management of extremely large and complex data collections.
• MKL: Developed specifically for science, engineering, and financial computations, Intel™ Math Kernel Library (MKL) is a set of threaded and vectorized math routines that work to accelerate various math functions and applications.
• LLVM: The LLVM Project is a collection of modular and reusable compiler and toolchain technologies.

Installation #

The Anaconda site will do a much better job of this than I can. Saying that, my first installation failed, and I had to rm the files and start again (the docs show the old location, it's now rm -rf ~/opt/anaconda3).

Anaconda Navigator #

Introduced above, this provides a nice visual UI for running a set of tools, and managing dependancies. From the main window, you can launch a Jupyter notebook and start coding.

You're now well set for building notebooks locally.

• Next: Jupyter notebooks for machine learning