LightGBM/docker/gpu/README.md

59 строки
1.7 KiB
Markdown

# 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.
---
# 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.
---
# 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 (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
```