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https://github.com/fatchur/docker-and-kubernetes-note
Opencv, Opencv-contribt, Tensorflow-GPU, Keras, Zeromq, and DS-Tools docker
https://github.com/fatchur/docker-and-kubernetes-note
computer-vision-docker deep-learning-docker docker docker-compose-note dockerfile dockerhub-note kubernetes-note mysql-docker opencv opencv-cotrib-docker opencv-docker tensorflow-gpu-docker ubuntu1604-docker ubuntu1804-docker
Last synced: 3 months ago
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Opencv, Opencv-contribt, Tensorflow-GPU, Keras, Zeromq, and DS-Tools docker
- Host: GitHub
- URL: https://github.com/fatchur/docker-and-kubernetes-note
- Owner: fatchur
- Created: 2017-05-16T06:26:18.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2020-03-04T09:53:00.000Z (almost 5 years ago)
- Last Synced: 2024-10-11T16:22:58.399Z (3 months ago)
- Topics: computer-vision-docker, deep-learning-docker, docker, docker-compose-note, dockerfile, dockerhub-note, kubernetes-note, mysql-docker, opencv, opencv-cotrib-docker, opencv-docker, tensorflow-gpu-docker, ubuntu1604-docker, ubuntu1804-docker
- Language: CSS
- Homepage:
- Size: 23.1 MB
- Stars: 7
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## All-in-one Docker image for Deep Learning
Here are Dockerfiles to get you up and running with a fully functional deep learning machine. It contains all the popular deep learning frameworks with CPU and GPU support (CUDA and cuDNN included). The CPU version should work on Linux, Windows and OS X. The GPU version will, however, only work on Linux machines. See [OS support](#what-operating-systems-are-supported) for details
If you are not familiar with Docker, but would still like an all-in-one solution, start here: [What is Docker?](#what-is-docker). If you know what Docker is, but are wondering why we need one for deep learning, [see this](#why-do-i-need-a-docker)
## Specs
### For Ubuntu 18.04 Docker
This is what you get out of the box when you create a container with the provided image/Dockerfile:
* Ubuntu 18.04
* [CUDA 9.2](https://developer.nvidia.com/cuda-toolkit)
* [cuDNN 7](https://developer.nvidia.com/cudnn)
* [Tensorflow-gpu 1.12](https://www.tensorflow.org/)
* [Keras](http://keras.io/)
* [iPython/Jupyter Notebook for python 2 and 3](http://jupyter.org/)
* [Numpy](http://www.numpy.org/), [SciPy](https://www.scipy.org/), [Pandas](http://pandas.pydata.org/), [Scikit Learn](http://scikit-learn.org/),
[Matplotlib](http://matplotlib.org/), [Seaborn](https://seaborn.pydata.org/), [Xgboost](https://xgboost.readthedocs.io/en/latest/), [Statmodels](https://pypi.org/project/statsmodels/), requests, imutils
* [gunicorn web server](http://docs.gunicorn.org/en/stable/install.html)
* [OpenCV 3.4.2 + Opencv contribt](http://opencv.org/)
* [Flask](http://flask.pocoo.org/)
* Mysql-connector
### For Ubuntu 16.04 Docker
This is what you get out of the box when you create a container with the provided image/Dockerfile:
* Ubuntu 16.04
* [CUDA 9](https://developer.nvidia.com/cuda-toolkit)
* [cuDNN 7](https://developer.nvidia.com/cudnn)
* [Tensorflow-gpu 1.12](https://www.tensorflow.org/)
* [Keras](http://keras.io/)
* [iPython/Jupyter Notebook for python 2 and 3](http://jupyter.org/)
* [Numpy](http://www.numpy.org/), [SciPy](https://www.scipy.org/), [Pandas](http://pandas.pydata.org/), [Scikit Learn](http://scikit-learn.org/),
[Matplotlib](http://matplotlib.org/), [Seaborn](https://seaborn.pydata.org/), [Xgboost](https://xgboost.readthedocs.io/en/latest/), [Statmodels](https://pypi.org/project/statsmodels/), requests, imutils
* [gunicorn web server](http://docs.gunicorn.org/en/stable/install.html)
* [OpenCV 3.4.2 + Opencv Contribt](http://opencv.org/)
* [Flask](http://flask.pocoo.org/)
* Mysql-connector
### Gogle Cloud K80 GPU Note
* **You should install nvidia driver**
```
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.0.176-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1604_9.0.176-1_amd64.deb
sudo apt-get update
```
* **Sincronize the driver**
```
sudo nvidia-smi -pm 1
```
* **Check the Driver**
```
nvidia-smi
```
* **If these steps don't work, try to install automatically by this:**
```
sudo apt install ubuntu-drivers-common
```
* **Or this:**
```
sudo ubuntu-drivers autoinstall
```
* **The last, BUILD YOUR DOCKER FILE**
### CUDA and CUDNN Note For Tensorflow
> Tensorflow 1.4.x: CUDNN 6.0 and CUDA 8.0
> Tensorflow >= 1.5: CUDNN 7.0 and CUDA 9.0