hfg-tech-dive

Fantastic Algorithms and where to find them

Machine learning tools come in various shapes and forms, and some are available in a ready-to-use package. RunwayML provides a collection of such tools. Not all current tools are available though and some need to be paid for. To take full control over the scripts you are using, you can go directly to the source.

Most machine learning tools are developed, updated and distributed via github. github is a hosting service where you can host git repositories.

git is a software that lets developers organise their code base in a way that keeps track of different versions of the code - it's like saving the state of a whole directory, so you can restore previous versions of your project. git doesn't store the files on your computer alone, but puts them on a server where the server can handle back-ups and where other developers can pull the newest version of the project or push their addition to the project to. git is version control, back-up, and collaborative workspace all in one. A folder that is tracked by git is called a git repository.

A git repository hosted on github can be accessed either by the web front end through your browser or by the git software, usually from the terminal. From the website, you can download the files and run the script. If you instead use git to access the repository, you also get the project history, different versions and updates in the future.

Install git following these instructions.

StyleGAN

Let's have a look at the StyleGAN landing page. It links to a github-repo with the code as well as to a repo with the data set plus a scientific paper explaining the algorithm from a conceptual point of view. The video previews can give you an indication about whether this algorithm is useful for your project or not.

Ingredients of a Machine Learning Algorithm

A ml-algorithm usually consists of three parts: the code, the data set, and the model. The code either uses the data set to train the model or it uses a model to make the model do what it was trained for. You can either use an existing model for your project, which is rather easy. Interesting results can be achieved by using the model in an unintended way, by hacking the model. Or you can train your own model, which is rather hard but gives you deeper control over how the model will integrate into your project.

Where do I begin?

Now you have access to a script that looks promising. How do you start it? Most algorithms are written in a programming language called python. To start python, go to your terminal and type in python. If python is already installed on your machine, this opens an interactive session where you can type python code and it will immediately be evaluated. If python is not yet installed on your machine, follow these instructions.

Additionally, most machine learning scripts make extensive use of code that has already be written and manages standard tasks of certain domains, so-called libraries or frameworks. Depending on the script you are running, you might need to get a few of those, too. Notable libraries for machine learning are tensorflow, numpy, scipy, torch, keras, and many more. Unfortunately, the requirements of some scripts might collide, for example if one script depends on tensorflow v1.0 and another script depends on tensorflow v2.1 - which version should you then install? Well, to manage different dependencies, people use virtual environments. With tools like anaconda or virtualenv you can activate an environment from scratch and install all dependencies needed for a given script. If you then want to run another script with conflicting dependencies, you can deactivate that first environment, activate a new one and install the dependencies as needed. You will end up with different environments for different scripts, and you can switch between those depending on which script you want to run.

But really, how do I begin?

With python in place, your virtual environments preconfigured and the git repository cloned to your hard drive, to run a script, you have to activate the respective virtual environment and then run the script by calling the main .py-file with python like so:

python generate_figures.py

The name of the main file might differ and there might be different files to run for different tasks, as for example the StyleGAN repository has a separate file for starting the training. Also, the behaviour of a script can usually modified by either setting the options in a configuration file (often called config.py) or calling the script with so-called flags, or both. Calling a script with flags looks like this:

python pix2pix.py --mode train --output_dir facades_train

Usually a repository comes with a README-file that gives more or less detailed information on how to set options for a given script. If the script had a lot of exposure on the internet, you also might find accompanying blog posts that might give hints on successful use, as for example this blog does for StyleGAN and this blog does for pix2pix.

Did I already mention...?

All this stuff usually happens in the terminal. Some people also call it the command line, bash, or the shell. There are subtle differences between those names that we will ignore for today.

• On Mac, you can open a terminal by using the spotlight (Cmd+Space) and typing in terminal
  • open a new terminal tab with Cmd+T
  • close current tab with Cmd+W an
  • close the terminal by typing Cmd+Q
• On Ubuntu, you can open a terminal by typing Ctrl+Alt+T
  • open a new tab with Ctrl+Shift+T
  • and close a tab by typing exit - this will also close the terminal, if the last tab is exited.

Mac and Linux share most of the commands in the terminal, so if you know your way around in one operating system, you will already have a huge head start with the other. This does not apply to Windows, though. Here, the instruction set is very different and I don't know much about it ¯\_(ツ)_/¯.

Why bother?

But why should you even bother going through all this trouble with the terminal? Well, two reasons. First, if you really want to take control over the tools that are available as open source projects on the internet, the terminal gives you access on a very detailed level. Second, since machine learning centers around having lots and lots of data handy, it is very helpful, if not crucial to the success of a project, to be able to move, copy, evaluate and manipulate data fast. Imagine going through an image folder of 2000 images and checking manually if they are all of the same size. A command line script can do that for you in 3 lines. Unfortunately, a command line script can also easily overwrite, corrupt or delete your data, and usually not in a way that you could restore it from the waste bin. With great power comes great responsibility. On the other hand, if your project does not need to have much control over the data or the script, then it might just be fine to just skim over the details and use a well-tested tool with a nice user interface.

Your turn.

As a small exercise and to prepare for Wednesday, use the break to install git, python, and anaconda. Then clone this git repository to your computer and try to run hello_world.py. It probably won't. Figure out what's missing and use anaconda to configure a virtual environment so you can run the script.