The easiest way to get started with ForML is to go through its practical tutorials presented in
this chapter. Assuming you have already installed ForML as per the installation instructions (ensure you have at least the
rest extra features installed) and ideally also familiarized yourself with the ForML
principles, you can now go straight through the following list of step-by-step
examples demonstrating the ForML capabilities.
The tutorials depend on the following initial environment configuration:
Assuming you have no existing feeds configured in your system yet, let’s install the Openlake feed:
$ pip install --constraints https://raw.githubusercontent.com/formlio/openlake/main/constraints.txt 'openlake[kaggle]'
Let’s now configure the local ForML platform by adding the following content to
[RUNNER] default = "compute" [RUNNER.compute] provider = "dask" scheduler = "threads" [RUNNER.visual] provider = "graphviz" format = "png" [REGISTRY] default = "tutorial" [REGISTRY.tutorial] provider = "posix" path = "/tmp/forml-tutorial/registry" [FEED] default = ["openlake"] [FEED.openlake] provider = "openlake:Lite" [SINK] default = "print" [SINK.print] provider = "stdout" [INVENTORY] default = "tutorial" [INVENTORY.tutorial] provider = "posix" path = "/tmp/forml-tutorial/inventory" [GATEWAY] default = "http" [GATEWAY.http] provider = "rest" port = 8080 processes = 2
Make sure to configure your Kaggle API access token under the
as described in the Kaggle API Documentation to get
access to all of the datasets used in this tutorial.
Your local environment is now ready to perform all the runtime actions demonstrated in these tutorials.
The list of available tutorials is:
The Titanic challenge is a complete end-to-end ML project implemented using ForML.
Pipeline demos represent a set of small snippets demonstrating the pipeline composition fundamentals.