Data Science and Machine Learning

The Data Science and Machine Learning environments are designed for a variety of data science tools and machine learning libraries. These include environments for xgboost, lightgbm, scikit-learn, scipy, pandas, numpy, matplotlib, seaborn, numba, and cupy. Each environment is equipped with the necessary tools for efficient data analysis, machine learning model training, and numerical computations.

Environment	Description	Quickstart
datascience	Environment equipped with tools like xgboost, lightgbm, scikit-learn, and scipy, pandas, numpy, matplotlib, seaborn, numba, and cupy.
rapidsai	Environment designed for RAPIDS.ai tools like cuDF, cuML, cuGraph, all powered by NVIDIA GPUs.
cupy	Environment set up for CuPy, a GPU-accelerated library for numerical computations.
numba	Environment equipped with Numba, a just-in-time compiler for Python that helps developers accelerate scientific computing with GPUs.
scipy	Environment designed for SciPy, a Python library used for scientific and technical computing.
sklearn	Environment for Scikit-learn, a machine learning library in Python.
xgboost	Environment for XGBoost, a scalable and flexible gradient boosting library that is GPU-compatible.
lightgbm	Environment for LightGBM, a gradient boosting framework that uses tree-based learning algorithms. Supports parallel, distributed, and GPU learning.