You can work on the exercises on your local machine, or in the cloud using Google Colab. Dependent on your choice, please select the corresponding tab in the following tutorial. If you know how to work with anaconda and are ok with an anaconda environment taking more than 1GB of disk space on your machine, I would recommend you to use your local machine. However, there is one deep learning exercise where you temporarily might want to switch to Colab, if you do not own a GPU.
Downloading the exercises¶
git clone https://github.com/thomasfermi/Algorithms-for-Automated-Driving.git
Otherwise visit this book’s github repo and click on the green button that says “Code”. In the pop-up menu, please select “Download zip”. Extract the zip to a directory of your choice.
Nothing more to do.
Open Google Drive. In the top left navigation you can see “My Drive”. Right click “My Drive” and select “New folder”. Name this folder “aad”. You will see the folder appear. Double-click it. Now open a file explorer on your computer and navigate to the folder “Algorithms-for-Automated-Driving” that you have downloaded from github. Select all folders except the “book” folder and drag and drop them into the empty “aad” folder in your Google Drive.
If you do not have anaconda, please download and install it.
Please create a conda environment called
aad (Algorithms for Automated Driving) for this course using the environment.yml file within “Algorithms-for-Automated-Driving/code”
cd Algorithms-for-Automated-Driving/code conda env create -f environment.yml
Tip: Use mamba!
You may find that creating a conda environment takes a lot of time. I recommend to install mamba:
conda install mamba -n base -c conda-forge
Installing mamba takes some time, but afterwards setting up environments like the one for this book is way faster. Just write
mamba instead of
mamba env create -f environment.yml
Be sure to activate that environment to work with it
conda activate aad
If you are working on Windows, consider adding anaconda to your PowerShell.
When you run code in Google Colab, you will have most of the libraries you need already installed. Just import whatever you need. If it is missing, you will get an error message that explains how to install it.