This repo explores a Kaggle competition called TReNDS Neuroimaging We will be predicting multiple assessments (as well as age) from multimodal brian MRI features.
Go to the wiki for more details on the problem set and modeling work.
- Install Python, minimum 3.7.5 (pyenv is the easiest way to install specific versions)
- Python Environment/Dependency Management via poetry
- Install poetry - instructions
- DVC for Data Pipeline Management
- Install dvc - instructions
- Jupyter Notebook environment via Docker
A lot of opinions were pulled from here
Because of the repository structure and poetry for dependency management, you can leverage auto-completion within the IDE and leverage that same code within the Jupyter Notebooks. In addition, the python packages you pull in will be the same versions between your IDE and the Jupyter Notebook.
If using PyCharm, you'll need to set the project interpreter as the virtualenv that was created when getting
dependencies installed. To do this, run poetry env list --full-path to get the full directory path of the
Python environment.
Navigate into the root of the cloned directory, and run docker-compose up to launch a Jupyter Notebook server. If you
change any dependencies through poetry, you'll need to rebuild the container via docker-compose build.
Note that the Jupyter notebook by default sets the root to be the repository root, so all files that are used for notebook development should be within the repository.
If you'd like to have a different port for your Jupyter server, you can run JUPYTER_PORT=<new port> docker-compose up
to use whatever port you'd like. The default messaging will still say port 8888, but it's allocated to whatever port
you pass in.
Leverage the following code snippet to use the src directory in notebooks
import sys
# This should navigate to the repository root
sys.path.append('../')
%reload_ext autoreload
%autoreload 2