Create a flask application for ML Ops
to instal pycaret
Install
PyCaret is tested and supported on the following 64-bit systems:
Python 3.6 – 3.8
Python 3.9 for Ubuntu only
Ubuntu 16.04 or later
# Windows 7 or later
Install PyCaret with Python's pip package manager.
pip install pycaret
To install the full version (see dependencies below):
pip install pycaret[full]
If you want to try our nightly build (unstable) you can install pycaret-nightly from pip.
pip install pycaret-nightly
Environment
In order to avoid potential conflicts with other packages, it is strongly recommended to use a virtual environment, e.g. python3 virtualenv (see python3 virtualenv documentation) or conda environments. Using an isolated environment makes it possible to install a specific version of pycaret and its dependencies independently of any previously installed Python packages.
# create a conda environment
conda create --name yourenvname python=3.8
# activate conda environment
conda activate yourenvname
# install pycaret
pip install pycaret
# create notebook kernel
python -m ipykernel install --user --name yourenvname --display-name "display-name"
PyCaret is not yet compatible with sklearn>=0.23.2.
# For MAC OS
MAC users will have to install LightGBM separately using Homebrew, or it can be built using CMake and Apple Clang (or gcc). See the instructions below:
Install CMake (3.16 or higher):
>> brew install cmake
Install OpenMP
>> brew install libomp
Run the following commands in terminal:
git clone --recursive https://github.com/microsoft/LightGBM ; cd LightGBM
mkdir build ; cd build
cmake ..
make -j4
install the docker extension in vs studio code or install the docker application on the local machine.