pasassi/multiphaseml

Multip-hase flow regime identification with ML models

multiphaseML

Repository for applying Machine-Learning techniques to multi-phase flows:

• Flow regime recognition

Installation in Linux OS

1. Clone the repo in the desired drectory

   git clone [email protected]:pasassi/multiphaseML.git

2. Create a virtual environment (venv must be installed)

   i. Create a virtual environment (here is named .venv)

   python3 -m venv .venv

   ii. Activate it

   source .venv/bin/activate

3. Install necessary dependencies via requirements.txt (I recommend pipenv)

   pipenv install

   This will read the requirements.txt file and generate Pipfile and Pipfile.lock files

4. Add your virtual environment to Jupyter (the interactive IDE)

   python -m ipykernel install --user --name=.venv

5. Add your virtual environment as preferred python interpret in VSC

   i. Create a folder called .vscode in the repo root dir if you haven't already.

   mkdir .vscode

   ii. Create the user settings file inside that folder

   touch ./.vscode/settings.json

   iii. Add this line to your settings.json file to set the default Python interpreter for VS Code.

   // Sets this project's .venv Python interpreter
   {
     "python.defaultInterpreterPath": "<path_to_multiphaseML>/multiphaseML/.venv/bin/python3",
   }

   Where <path_to_multiphaseML> is the absolute path to multiphaseML repository.

TensorFlow in GPU

Follow the installation guide for using TensorFlow with GPU