Installation Guide
We recommend installing HyperGBM with conda
or pip
. It’s also possible to install and use HyperGBM in a Docker container if you have a Docker environment.
As for software, Python version 3.6 or above is necessary to install HyperGBM.
Using Conda
Install HyperGBM with conda
from the channel conda-forge:
conda install -c conda-forge hypergbm
On the Windows system, recommend install pyarrow(required by hypernets) 4.0 or earlier version with HyperGBM:
conda install -c conda-forge hypergbm "pyarrow<=4.0"
Using Pip
Install HyperGBM with different pip
options:
Typical installation:
pip install hypergbm
To run HyperGBM in JupyterLab/Jupyter notebook, install with command:
pip install hypergbm[notebook]
To support experiment visualization base on web, install with command:
pip install hypergbm[board]
To run HyperGBM in distributed Dask cluster, install with command:
pip install hypergbm[dask]
To support dataset with simplified Chinese in feature generation,
Install
jieba
package before running HyperGBM.OR install with command:
pip install hypergbm[zhcn]
Install all above with one command:
pip install hypergbm[all]
Using Docker
It is possible to use HyperGBM in a Docker container. To do this, users can install HyperGBM with pip
in the Dockerfile. We also publish a mirror image in Docker Hub which can be downloaded directly and includes the following components:
Python 3.8
HyperGBM and its dependent packages
JupyterLab
Docker image tag naming scheme:
<hypergbm_version>: Python + JupyterLab + HyperGBM + HyperGBM notebook plugins
<hypergbm_version>-cuda<cuda_version>-cuml<cuml_version>: above + CUDA toolkit + cuML
<hypergbm_version>-cuda<cuda_version>-cuda<cuml_version>-lgbmgpu: above + GPU enabled LightGBM
Download the docker image:
docker pull datacanvas/hypergbm
Run a docker container:
docker run -ti -e NotebookToken="your-token" -p 8888:8888 datacanvas/hypergbm
Then one can visit http://<your-ip>:8888
in the browser and type in the default token to start.
Requirements for GPU acceleration
cuML and cuDF
HyperGBM accelerates data processing with NVIDIA RAPIDS cuML and cuDF. Please install them before running HyperGBM on GPU. For detailed instructions, check the link Get RAPIDS.
LightGBM with GPU support
Default installation of LightGBM does not support GPU training. Please ensure LightGBM with GPU support before installing HyperGBM. For detailed instructions, check the link LightGBM GPU Tutorial.
XGBoost and CatBoost with GPU support
Default installations of XGBoost and CatBoost have supported GPU training. However, if you build them from source code, please enable GPU support.