nielsborie/ml-docker repository overview

⁠ml-docker

⁠An all-in-one Docker image for machine learning.

⁠Specs

This is what you get out of the box when you create a container with the provided image/Dockerfile:

• Ubuntu 18.04
• GCC/G++ 7
• Java 8
• Jupyter Notebook⁠
• Conda Python 3.6.x⁠
• Python Data Analysis : Numpy⁠, Pandas⁠, Matplotlib⁠, SciPy⁠, Seaborn⁠, Yellowbrick⁠
• Python Machine Learning : scikit-learn⁠, xgboost⁠, LightGBM⁠, Catboost⁠, mlxtend⁠
• Python NLP : gensim⁠, spaCy⁠
• Python Auto-ML : TPOT⁠, h2o-automl⁠
• Python Hyper-Optimizer : hyperopt⁠, sklearn-deap⁠
• Python Deep Learning : Tensorflow⁠, Keras⁠
• Python interpretability : shap⁠, skope-rules⁠, lime⁠
• Python Genetic Programming : gplearn⁠
• Python Topological Data Analysis (TDA) : KeplerMapper⁠
• h2o plateform : h2o⁠

⁠Setup

⁠Prerequisites

Install Docker following the installation guide for your platform: here⁠

⁠Obtaining the Docker image

• ⁠Option 1: Download the Docker image from Docker Hub

Available here : (https://hub.docker.com/r/nielsborie/ml-docker/⁠)

docker pull nielsborie/ml-docker

• ⁠Option 2: Build the Docker image locally

1. ⁠clone the repository

git clone https://github.com/nielsborie/ml-docker.git
cd /ml-docker

1. ⁠basic building :

docker build . -t ml-docker

1. ⁠If you have a proxy issue execute the following line :

docker build . --no-cache --force-rm --build-arg http_proxy=<proxy> --build-arg https_proxy=<proxy> --build-arg no_proxy=localhost,<proxy>,<proxy>,.an.local -t ml-docker

⁠Running the Docker image as a Container

Once we've built the image, we have all the frameworks we need installed in it. We can now spin up one or more containers using this image.

• ⁠Basic run

Simplest command to launch the container.

docker run --name ML-env -p 8887:8888 nielsborie/ml-docker

⁠If you don't want to be stick to your terminal, you can run it in detached mode (-d)

docker run --name ML-env -d -p 8887:8888 nielsborie/ml-docker

• ⁠Start & Stop

Once you create the container, all you need to do is launch it :

docker start ML-env
docker stop ML-env

• ⁠Enter in the running container

Since you launched the container, you can get in and be root inside the image!

docker exec -it ML-env /bin/bash

• ⁠Customize your Container

• ⁠If you want a real password (and avoid copy/paste token step...)

docker run --name ML-env -d -p 8887:8888 -d nielsborie/ml-docker start-notebook.sh --NotebookApp.password="sha1:b6dba7097c97:7bded30fcbd5089adb3b63496d5e68921e102a5f" 

• ⁠If you want to share your current working folder, you can map it with "-v" or "--volume"

docker run --name ML-env -p 8887:8888 -d -v /sharedfolder:/home/jovyan/work/ -e NB_UID=<your-UID> --user root nielsborie/ml-docker start-notebook.sh --NotebookApp.password="sha1:b6dba7097c97:7bded30fcbd5089adb3b63496d5e68921e102a5f"

⁠If you need to change the password check :

Jupyter notebook documentation⁠

⁠You can find additionnal info here :

Jupyter docker documentation⁠