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https://github.com/autuanliu/ml-docker-env
:books::pineapple: This is my personal docker configure for machine learning and deep learning
https://github.com/autuanliu/ml-docker-env
deep-learning docker docker-image dockerfile machine-learning
Last synced: about 1 month ago
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:books::pineapple: This is my personal docker configure for machine learning and deep learning
- Host: GitHub
- URL: https://github.com/autuanliu/ml-docker-env
- Owner: AutuanLiu
- License: apache-2.0
- Created: 2017-11-27T15:18:15.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2021-04-29T19:03:55.000Z (over 3 years ago)
- Last Synced: 2023-09-27T10:45:42.832Z (over 1 year ago)
- Topics: deep-learning, docker, docker-image, dockerfile, machine-learning
- Language: Dockerfile
- Homepage: http://bit.ly/2Asn180
- Size: 57.6 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## machine learning Docker environment
[](https://hub.docker.com/u/autuanliu/)
[]()
* CI info
Docker tag | status
--- | ---
CPU | [![Build Status][1]][2]
GPU | [![Build Status][1]][2]
This is a Docker configure for machine learning and deep learning. You can use the Dockerfile to build Docker image locally or
pull the image I have create from [Docker Hub](https://hub.docker.com/r/autuanliu/)
1. [CPU Version](https://hub.docker.com/r/autuanliu/ml-docker-env-cpu/)
2. [GPU Version](https://hub.docker.com/r/autuanliu/ml-docker-env-gpu/)
### Pull the image
1. from Docker hub
* CPU-Version
```bash
docker pull autuanliu/ml-docker-env-cpu
```
* GPU-Version
```bash
docker pull autuanliu/ml-docker-env-gpu
```
2. from Aliyun
* CPU-Version
```bash
docker pull registry.cn-hangzhou.aliyuncs.com/autuanliu/ml-env-cpu
```
* GPU-Version
```bash
docker pull registry.cn-hangzhou.aliyuncs.com/autuanliu/ml-env-gpu
```
for more information about Docker, you can visit the website: [Docker - Build, Ship, and Run Any App, Anywhere](https://www.docker.com/)
### what you can get
when you create a container with the docker image, you can get:
* Ubuntu 16.04
* nano
* tmux
* Tensorflow
* Pytorch
* Keras
* Anaconda
* jupyter notebook
* R language support
* numpy
* pandas
* matplotlib
* seaborn
* pillow
* Other useful Python libraries
* Other useful R libraries for data science
If you want to add new modules of python or other libraries, you can modify the Dockerfile
directly or submit a issue [here](https://github.com/AutuanLiu/ML-Docker-Env/issues)
### Usage
```bash
docker run -it --rm -p 8888:8888 -v your_host_work_dir:/home/autuanliu/sharef registry_name/image_name:tag
```
* Notes:
1. TensorBoard port 6006
2. You do not have root privileges
3. Share folder: /home/autuanliu/sharef/
* here is an example
[](http://bit.ly/usage-demo)
for more detail commands usage, you can visit the website: [Get Started, Part 1: Orientation and setup | Docker Documentation](https://docs.docker.com/get-started/)
### About
These Docker images are used for learning and research related to data science and machine learning. You do not need to configure a complex environment on your computer. You need almost everything are all included in the images. All you need is `docker pull` and `docker run`.
[Another repository](http://bit.ly/2jOp8uh) are created to submit my learning and research. It is about Machine learning, Neural Network, data science etc. You can run all code or jupyter-notes base on this Docker image.
----
If you have any questions or suggestions about this reposity, you can submit your issues or email me. [[email protected]]
[1]:https://travis-ci.org/AutuanLiu/ML-Docker-Env.svg?branch=master
[2]:https://travis-ci.org/AutuanLiu/ML-Docker-Env