README.md

# Tiny Distroless Dockerfile for LightGBM GPU CLI-only Version

`dockerfile-cli-only-distroless.gpu` - A multi-stage build based on the `nvidia/opencl:devel-ubuntu18.04` (build) and `distroless/cc-debian10` (production) images. LightGBM (CLI-only) can be utilized in GPU and CPU modes. The resulting image size is around 15 MB.

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# Small Dockerfile for LightGBM GPU CLI-only Version

`dockerfile-cli-only.gpu` - A multi-stage build based on the `nvidia/opencl:devel` (build) and `nvidia/opencl:runtime` (production) images. LightGBM (CLI-only) can be utilized in GPU and CPU modes. The resulting image size is around 100 MB.

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# Dockerfile for LightGBM GPU Version with Python

`dockerfile.gpu` - A docker file with LightGBM utilizing nvidia-docker. The file is based on the `nvidia/cuda:8.0-cudnn5-devel` image.
LightGBM can be utilized in GPU and CPU modes and via Python.

## Contents

- LightGBM (cpu + gpu)
- Python 3.8 (conda) + scikit-learn, notebooks, pandas, matplotlib

Running the container starts a Jupyter Notebook at `localhost:8888`.

Jupyter password: `keras`.

## Requirements

Requires docker and [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) on host machine.

## Quickstart

### Build Docker Image

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

### Run Image

```sh
nvidia-docker run --rm -d --name lightgbm-gpu -p 8888:8888 -v /home:/home lightgbm-gpu
```

### Attach with Command Line Access (if required)

```sh
docker exec -it lightgbm-gpu bash
```

### Jupyter Notebook

```sh
localhost:8888
```