Intel MacOS

Note: This guide is suitable for Mac machines with Intel processors. For newer Macs with M1 processors, see M1 MacOS.

Python installation

The first thing you need to do is to set up python 3.8 or 3.9.

$ brew install python3.8

Also, make sure that pip is installed. (You can check this by running pip help). If not, use the below command to install:

python3 -m ensurepip --upgrade

Along with python, you also need to install libomp. Note that version 12.0.0 of libomp on Homebrew is incompatible with LightGBM on MacOS. We recommend running the following brew commands for getting the required version:

curl https://raw.githubusercontent.com/Homebrew/homebrew-core/fb8323f2b170bd4ae97e1bac9bf3e2983af3fdb0/Formula/libomp.rb -o libomp.rbbrew unlink libompbrew install libomp.rb

Bitfount Installation

Our recommendation is to install and use Bitfount in a virtual environment, see instructions on how to do this here.

You can then proceed to install bitfount.

pip install bitfount

This might take a few moments, so do not worry if it does — grab a coffee or tea while you wait!

If you are planning on using the bitfount package with Jupyter Notebooks we recommend you install the splinter package bitfount[tutorials] which will make sure you are running compatible jupyter dependencies.

pip install 'bitfount[tutorials]'

If you want to use differential privacy (DP), you will need to install the DP extras as well:

pip install 'bitfount[dp]'

Next Steps

Once you’ve installed Bitfount, whichever party that will provide the data for analysis must connect the data to a Pod. See For Data Custodians for detailed instructions.

If data is already connected to a Pod for analysis and you wish to train models on or query it, see For Data Scientists.