Contains source code and Shell scripts to generate and deploy example DVC repositories used in the Get Started and other sections of the DVC docs.
Please make sure you have these available on the environment where these scripts will run:
python3 and pip commands)In order to have a consistent naming scheme across all example repositories, the new repositories should be named as:
example-PROD-FEATUREwhere PROD is one of the products like dvc, cml, studio, or dvclive, and FEATURE is
the feature that the repository focused on, like experiments, or pipelines.
You can also use additional keywords as suffix to differentiate from the others.
⚠️ Please create all new repositories with the prefix example-.
Each example DVC project is in each of the root directories (below). cd into
the directory first before running the desired script, for example:
$ cd example-get-started
$ ./deploy.shThere are 2 GitHub Actions set up to test and deploy the project:
These will automatically test and deploy the project. If you need to run the project
locally/manually, you only directly need generate.sh. deploy.sh is a helper script
run within generate.sh.
generate.sh: Generates the example-get-started DVC project from
scratch.
By default, the source code archive is derived from the local workspace for development purposes.
For deployment, use generate.sh prod to upload/download a source code
archive from S3 the same way as in Connect Code and
Data.
deploy.sh: Makes and deploys code archive from
example-get-started/code to use for generate.sh.
By default, makes local code archive in example-get-started/code.zip.
For deployment, use deploy.sh prod to upload to S3.
Requires AWS CLI and write access to
s3://dvc-public/code/get-started/.
There are 2 GitHub Actions set up to test and deploy the project:
These will automatically test and deploy the project. If you need to run the project locally/manually, run generate.sh.
Even after automatic deployment, you still need to follow the instructions to: