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README.md

Using LightGBM via Docker

This directory contains Dockerfile to make it easy to build and run LightGBM via Docker.

Installing Docker

Follow the general installation instructions on the Docker site:

Using CLI Version of LightGBM via Docker

Build a Docker image with LightGBM CLI:

mkdir lightgbm-docker
cd lightgbm-docker
wget https://raw.githubusercontent.com/Microsoft/LightGBM/master/docker/dockerfile-cli
docker build -t lightgbm-cli -f dockerfile-cli .

where lightgbm-cli is the desired Docker image name.

Run the CLI from the container:

docker run --rm -it \
--volume $HOME/lgbm.conf:/lgbm.conf \
--volume $HOME/model.txt:/model.txt \
--volume $HOME/tmp:/out \
lightgbm-cli \
config=lgbm.conf

In the above example, three volumes are mounted from the host machine to the Docker container:

  • lgbm.conf - task config, for example
app=multiclass
num_class=3
task=convert_model
input_model=model.txt
convert_model=/out/predict.cpp
convert_model_language=cpp
  • model.txt - an input file for the task, could be training data or, in this case, a pre-trained model.
  • out - a directory to store the output of the task, notice that convert_model in the task config is using it.

config=lgbm.conf is a command-line argument passed to the lightgbm executable, more arguments can be passed if required.

Running the Python-package Сontainer

Build the container, for Python users:

mkdir lightgbm-docker
cd lightgbm-docker
wget https://raw.githubusercontent.com/Microsoft/LightGBM/master/docker/dockerfile-python
docker build -t lightgbm -f dockerfile-python .

After build finished, run the container:

docker run --rm -it lightgbm