Contains material and instructions necessary to recreate the Machine Learning/AI hands-on exercises prepared for the BETTER project's session at the EO Joint Big Data Hackathon https://www.ec-better.eu/pages/h2020-eo-big-data-hackathon
Land cover changes and inter-annual vegetation performance analysis using ML algorithms
This experiment is hosted in a software repository.
Use git
to clone it:
cd /workspace
git clone https://github.com/ec-better/eohackathon-better-ai
cd better_ai
The file env_dmuk_ml.yml
contains the Python conda environment for running the notebooks contained in this folder.
From the shell, run:
conda env create --file=env_dmuk_ml.yml
Once the environment configuration is done, you can activate it:
conda activate env_dmuk_ml
Open the environment.ipynb
notebook and update the kernel to use env_dmuk_ml
Run the experiment by executing each of the cells with shift
+ Enter
.
If asked for the credentials, provide your Ellip username and associated Ellip API key.
This experiment is under version control and uses the git flow method (see [https://datasift.github.io/gitflow/IntroducingGitFlow.html])
If not done previously, clone the experiment repository:
git clone https://github.com/ec-better/eohackathon-better-ai
cd better_ai
Then, checkout the develop
branch with:
git checkout develop
At this stage, update the experiment.
When done:
git add -A
git commit -m '<commit message>'
git pull
Finally, do a release with:
ciop-release
The file env_better_ai.yml
contains the Python conda environment for running the notebooks contained in this folder.
From the shell, run:
conda env create --file=env_better_ai.yml
Once the environment configuration is done, you can activate it:
conda activate better_ai
Open the BetterAI.ipynb
notebook and update the kernel to use better_ai
Run the experiment by executing each of the cells with shift
+ Enter
.
Good luck!