python-M1TAL

Resources for Master TAL (NLP), first degree, at Inalco.

Installation – for those who like not to ask too many questions

With conda

1. Download Miniconda, a small version of Anaconda with conda, Python, and some useful packages:
   https://docs.conda.io/en/latest/miniconda.html

2. Get a clone of this repository:

$ git clone https://github.com/Alex-bzh/python-M1TAL.git
$ cd python-M1TAL

3. Run JupyterLab in its own environment:

$ conda env create -f environment.yml
$ conda activate tal-ml
$ jupyter-lab

With pixi

1. Install Pixi, a (modern) package management tool

2. Get a clone of this repository:

$ git clone https://github.com/Alex-bzh/python-M1TAL.git
$ cd python-M1TAL

3. Run JupyterLab in its own environment:

$ pixi install
$ pixi run start

Installation – for those who want to have fine control over what they install

Python 3 installation

1. First, check that Python is installed:

$ which python

If not, donwload the latest version: https://www.python.org/downloads/

2. Check the version of your Python distribution (at least 3.7):

$ python -V

If your version is older than 3.7, you may have a specific python3.7 binary:

$ which python3.7

If so, note the path and link it with the python command:

$ ln -s PYTHON3.7_PATH python

3. Be sure to have the latest version of pip, the Python package manager:

$ python -m pip install --user --upgrade pip

Installing a virtual environment

1. As venv is already included in the Python standard library, you just need to install a new virtual environment:

$ python -m venv tal-ml

2. Activate your environment:

$ source tal-ml/bin/activate

To leave you environment, just run the command deactivate.

Dependencies installation

With pip, install all the requested packages into your environment:

$ python -m pip install -r requirements.txt

Run JupyterLab

Simply run JupyterLab with a clone of this repository:

$ git clone https://github.com/Alex-bzh/python-M1TAL.git
$ cd python-M1TAL
$ jupyter-lab