bkavlak/gisgpu repository overview

⁠gisgpu_docker

• WORKS FOR LINUX DISTRIBUTION

Docker GPU Computing Container for GIS and Remote Sensing Applications. This container is based on ubuntu-18.04 and Python 3.6

If you are not familiar with Docker, please first watch this perfect tutorial: KodeKloud Docker Tutorial⁠ (First half is enough to understand below).

In case you want to add or remove python packages go to # PART - 3 Install Python Packages in the Dockerfile and add or remove the package below pip3 install.

⁠Package List:

imagecodecs
jupyterlab
numpy==1.16
scipy
Pillow
matplotlib
opencv-contrib-python
scikit-image
scikit-learn
xgboost
fiona
shapely
geopandas
rasterio
tifffile
tensorflow-gpu==2.1.0
keras

⁠Requirements:

• NVIDIA-docker dependencies as in here: NVIDIA-docker⁠

⁠PULL IMAGE:

In case you don't need to configure the Dockerfile

docker pull bkavlak/gisgpu:base

OR

⁠BUILD IMAGE:

In case you want to configure the Dockerfile.

Go to the directory where Dockerfile resides and type:

docker build -t 'NAME:TAG' .

⁠You should change text inside '___' as your preference

⁠RUN CONTAINER:

sudo docker run -it --name 'CONTAINER NAME' --gpus all -p 8888:8888 -p 6006:6006 -v 'VOLUME DIRECTORY':/edenazar/data 'NAME:TAG' bash

⁠You should change text inside '___' as your preference

You can add a volume to the container where you transfer files between the computer and the container.

⁠'VOLUME DIRECTORY' = where you put your files on the local machine

⁠ON BASH:

Run command starts the container and then you can run a Jupyter Notebook as below:

jupyter notebook --ip=0.0.0.0 --port=8888 --allow-root

If successful, you will see some links below.

http://127.0.0.1:888/?token......

Copy the last link to a browser (like Chrome). Hit ENTER.

Check whether GPU is identified by the container:

import tensorflow as tf

tf.config.experimental.list_physical_devices('GPU')

Enjoy!