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README.md
dockerfile-cli Add a Dockerfile for the command-line interface (#1008) Oct 31, 2017
dockerfile-python

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