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https://github.com/developmentseed/geo-ml-docker
Docker Image for Machine Learning with Geospatial Data in Python
https://github.com/developmentseed/geo-ml-docker
Last synced: 15 days ago
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Docker Image for Machine Learning with Geospatial Data in Python
- Host: GitHub
- URL: https://github.com/developmentseed/geo-ml-docker
- Owner: developmentseed
- Created: 2018-08-08T21:21:34.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2018-08-10T17:08:55.000Z (over 6 years ago)
- Last Synced: 2024-08-01T00:41:49.187Z (3 months ago)
- Language: Dockerfile
- Homepage:
- Size: 3.91 KB
- Stars: 6
- Watchers: 40
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# geo-ml-docker
This docker is built from [developmentseed/geolambda](https://github.com/developmentseed/geolambda) image which includes libraries and their dependencies for geospatial processing, such as numpy and gdal.
In addition to the libraries provided by geolambda, geo-ml-docker includes libraries for machine learning such as tensorflow and keras. See all included libraries in [requirements](./requirements.txt).
## Docker Commands
### On your local machine
- #### Build the image
```bash
docker build -t geo-ml-docker .
```
- #### Basic use case: Run a shell in a new container
```bash
docker run -it geo-ml-docker /bin/bash
```
- #### Advanced use case
Run a jupyter notebook in a new container, expose the port, and mount a volume. Note - this mounts a volume in the current directory so it should be run in the directory where any required files are maintained.
```bash
docker run -it -v ${PWD}:/code -p 8888:8888 geo-ml-docker /bin/bash -c "jupyter notebook --debug --ip=* --port=8888 --no-browser --allow-root"
```
### On an AWS EC2 machine
- #### Docker installation on the EC2 machine
Follow the AWS's [instruction](https://docs.aws.amazon.com/AmazonECS/latest/developerguide/docker-basics.html) to install Docker on the EC2 machine before you go ahead.
- #### Build the image
ssh to the EC2 with your pem, and build the docker image.
```bash
docker build -t geo-ml-docker .
```
- #### Run the jupyter notebook EC2
```bash
docker run -v ${PWD}:/code -it -p 8888:8888 geo-ml-docker /bin/bash -c "jupyter notebook --debug --ip=* --port=8888 --no-browser --allow-root"
```
- #### Log into the jupyter notebook from your local machine
In another terminal ssh to cloud machine.
```bash
ssh -i Your_AWS.pem -L 8888:localhost:8888 [email protected]
```
- #### Open your jupyter notebook on the browser
When you run the notebook the first time, copy and pasted something like `http://127.0.0.1::8888/?token=xxxxxx` to your browser to open the notebook.