Create a summary of the data (mean, range, variance, length, ...)
This algorithm is designed to be run with the vantage6 infrastructure for distributed analysis and learning.
Data is no longer loaded automatically inside compute functions. In a session, run a data extraction step first (for example read_csv from v6-extract-basics-py), then run summary with the desired columns and optional numeric_columns.
make imageTo finally run your algorithm on the vantage6 infrastructure, you need to create a Docker image of your algorithm.
The easiest way to create a Docker image is to use the GitHub Actions pipeline to automatically build and push the Docker image. All that you need to do is push a tag to the repository (only allowed for developers with write access to this repository).
A Docker image can be created by executing the following command in the root of your algorithm directory:
docker build -t [my_docker_image_name] .where you should provide a sensible value for the Docker image name. The
docker build command will create a Docker image that contains your algorithm.
You can create an additional tag for it by running
docker tag [my_docker_image_name] [another_image_name]This way, you can e.g. do
docker tag local_average_algorithm ghcr.io/vantage6/algorithm/average to
make the algorithm available on a remote Docker registry (in this case
ghcr.io/vantage6).
Finally, you need to push the image to the Docker registry. This can be done by running
docker push [my_docker_image_name]Note that you need to be logged in to the Docker registry before you can push
the image. You can do this by running docker login and providing your
credentials. Check this page
For more details on sharing images on Docker Hub. If you are using a different
Docker registry, check the documentation of that registry and be sure that you
have sufficient permissions.