If you don't operate inside a virtual environment, or only have access to an incompatible python version (<3.8), it is
recommended you create a virtual environment using conda:
conda env create -f environment.yml
conda activate deep-learning-tutorialsCreating the environment this way also takes care of installing the dependencies for you, so you can skip the rest of the setup and dive straight into one of the tutorials.
If you already have a python environment set aside for this project and just want to install the dependencies, you can do that using the following command:
pip install -e .Once you've went through the setup instructions above, you can start exploring the tutorial's notebooks. We recommend using JupyterLab to run the notebooks, which can be launched by running (from within your environment):
jupyter-labWhen you've launched JupyterLab's web interface, you can simply navigate to any of the tutorials listed below, and follow the instructions in there!
The datasets used in this tutorial (MNIST and ACDC) will automatically be downloaded at the beginning of the notebooks. However, if you experience issues with the download and want to download the datasets, you can find them here or download it with the following command:
gdown https://drive.google.com/uc?id=1H5pTOYjcSFR6B5GhA0sEPW0wgPVfBq8S -c -O data.tar.gzOnce downloaded, you may untar the file and and copy the data/ folder at the root of the project, i.e. at the same level as the src/ and the tutorials/ folders.
tar -xvzf data.tar.gzIf you want to contribute to the project, then you have to install development dependencies and pre-commit hooks, on top of the basic setup for using the project, detailed above. The pre-commit hooks are there to ensure that any code committed to the repository meets the project's format and quality standards.
# Install development dependencies
pip install -e .[dev]
# Install pre-commit hooks
pre-commit install