зеркало из https://github.com/microsoft/LightGBM.git
59 строки
1.7 KiB
Markdown
59 строки
1.7 KiB
Markdown
# Tiny Distroless Dockerfile for LightGBM GPU CLI-only Version
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`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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---
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# Small Dockerfile for LightGBM GPU CLI-only Version
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`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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---
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# Dockerfile for LightGBM GPU Version with Python
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`dockerfile.gpu` - A docker file with LightGBM utilizing nvidia-docker. The file is based on the `nvidia/cuda:8.0-cudnn5-devel` image.
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LightGBM can be utilized in GPU and CPU modes and via Python.
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## Contents
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- LightGBM (cpu + gpu)
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- Python (conda) + scikit-learn, notebooks, pandas, matplotlib
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Running the container starts a Jupyter Notebook at `localhost:8888`.
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Jupyter password: `keras`.
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## Requirements
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Requires docker and [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) on host machine.
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## Quickstart
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### Build Docker Image
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```sh
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mkdir lightgbm-docker
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cd lightgbm-docker
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wget https://raw.githubusercontent.com/Microsoft/LightGBM/master/docker/gpu/dockerfile.gpu
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docker build -f dockerfile.gpu -t lightgbm-gpu .
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```
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### Run Image
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```sh
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nvidia-docker run --rm -d --name lightgbm-gpu -p 8888:8888 -v /home:/home lightgbm-gpu
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```
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### Attach with Command Line Access (if required)
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```sh
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docker exec -it lightgbm-gpu bash
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```
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### Jupyter Notebook
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```sh
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localhost:8888
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```
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