LightGBM/docker

Using LightGBM via Docker

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

These builds of LightGBM all train on the CPU. For GPU-enabled builds, see the gpu/ directory.

Installing Docker

Follow the general installation instructions on the Docker site:

• macOS
• Ubuntu
• Windows

Using CLI Version of LightGBM via Docker

Build an image with the 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 \
    .

Once that completes, the built image can be used to run the CLI in a container. To try it out, run the following.

# configure the CLI
cat << EOF > train.conf
task = train
objective = binary
data = binary.train
num_trees = 10
output_model = LightGBM-CLI-model.txt
EOF

# get training data
curl -O https://raw.githubusercontent.com/Microsoft/LightGBM/master/examples/binary_classification/binary.train

# train, and save model to a text file
docker run \
  --rm \
  --volume "${PWD}":/opt/training \
  --workdir /opt/training \
  lightgbm-cli \
  config=train.conf

After this runs, a LightGBM model can be found at LightGBM-CLI-model.txt.

For more details on how to configure and use the LightGBM CLI, see https://lightgbm.readthedocs.io/en/latest/Quick-Start.html.

Running the Python-package Сontainer

Build an image with the LightGBM Python package installed.

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

Once that completes, the built image can be used to run LightGBM's Python package in a container. Run the following to produce a model using the Python package.

# get training data
curl -O https://raw.githubusercontent.com/Microsoft/LightGBM/master/examples/binary_classification/binary.train

# create training script
cat << EOF > train.py
import lightgbm as lgb
import numpy as np
params = {
    "objective": "binary",
    "num_trees": 10
}

bst = lgb.train(
    train_set=lgb.Dataset("binary.train"),
    params=params
)
bst.save_model("LightGBM-python-model.txt")
EOF

# run training in a container
docker run \
    --rm \
    --volume "${PWD}":/opt/training \
    --workdir /opt/training \
    lightgbm-python \
    python train.py

After this runs, a LightGBM model can be found at LightGBM-python-model.txt.

Or run an interactive Python session in a container.

docker run \
    --rm \
    --volume "${PWD}":/opt/training \
    --workdir /opt/training \
    -it lightgbm-python \
    python

Running the R-package Сontainer

Build an image with the LightGBM R package installed.

mkdir lightgbm-docker
cd lightgbm-docker
wget https://raw.githubusercontent.com/Microsoft/LightGBM/master/docker/dockerfile-r

docker build \
    -t lightgbm-r \
    -f dockerfile-r \
    .

Once that completes, the built image can be used to run LightGBM's R package in a container. Run the following to produce a model using the R package.

# get training data
curl -O https://raw.githubusercontent.com/Microsoft/LightGBM/master/examples/binary_classification/binary.train

# create training script
cat << EOF > train.R
library(lightgbm)
params <- list(
    objective = "binary"
    , num_trees = 10L
)

bst <- lgb.train(
    data = lgb.Dataset("binary.train"),
    params = params
)
lgb.save(bst, "LightGBM-R-model.txt")
EOF

# run training in a container
docker run \
    --rm \
    --volume "${PWD}":/opt/training \
    --workdir /opt/training \
    lightgbm-r \
    Rscript train.R

After this runs, a LightGBM model can be found at LightGBM-R-model.txt.

Run the following to get an interactive R session in a container.

docker run \
    --rm \
    -it lightgbm-r \
    R

To use RStudio, an interactive development environment, run the following.

docker run \
    --rm \
    --env PASSWORD="lightgbm" \
    -p 8787:8787 \
    lightgbm-r

Then navigate to localhost:8787 in your local web browser, and log in with username rstudio and password lightgbm.

To target a different R version, pass any valid rocker/verse tag to docker build.

For example, to test LightGBM with R 3.5:

docker build \
    -t lightgbm-r-35 \
    -f dockerfile-r \
    --build-arg R_VERSION=3.5 \
    .