Deep Learning Docker for Geospatial

Deep learning docker files and docker images for geospatial anaysis. It contains the most popular deep learning frameworks(PyTorch and Tensorflow) with CPU and GPU support (CUDA and cuDNN included). And some other commonly used packages in machine learning and geospatial anaysis.

Docker Hub: deepgeo

support docker image tags

• all-cpu-torch1.0.1-tf0.12.0
• all-cuda10-cudnn7-runtime-torch1.0.1-tf0.12.0
• all-cuda10-cudnn7-devel-torch1.0.1-tf0.12.0
• pytorch-1.0.1-cuda10-runtime
• pytorch-1.0.1-cuda10-devel
• tensorflow-0.12.0-cuda10-runtime
• tensorflow-0.12.0-cuda10-devel

packages contain

deep learning framework

• tensorflow
• keras
• pytorch

machine learning farmework

• scikit-learn
• scikit-image
• xgboost

geospatial packages

• GDAL
• fiona
• shapely
• rasterio
• tifffile
• geopandas

other related packages

• numpy
• scipy
• OpenCV
• Pillow
• jupyter
• matplotlib
• pandas

usage

build image

docker build -t REPOSITORY:TAG -f Dockerfile .

pull image

• cpu version: docker pull sshuair/deepgeo:[TAG]
• gpu version: docker pull sshuair/deepgao:[TAG]

start container

1. cpu: docker run -it --name [CONTAINER-NAME] -p 8888:8888 -p 6006:6006 -v /sharedfolder:/workdir sshuair/deepgeo:[TAG] bash
2. gpu: nvidia-docker run -it --name [CONTAINER-NAME] -p 8888:8888 -p 6006:6006 -v /sharedfolder:/workdir sshuair/deepgeo:[TAG] bash

jupyter notebook

If you want run jupyter notebook in a docker container you should use the follow command in a running docker container: jupyter notebook --allow-root