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. | |