CodeFlare Pipelines reimagined pipelines to provide a more intuitive API for the data scientist to create AI/ML pipelines, data workflows, pre-processing, post-processing tasks, and many more which can scale from a laptop to a cluster seamlessly.
See the API documentation here, and reference use case documentation in the Examples section.
A set of reference examples are provided as executable notebooks.
To run examples, if you haven’t done so yet, clone the CodeFlare project with:
git clone https://github.com/project-codeflare/codeflare.git
Example notebooks require JupyterLab, which can be installed with:
pip3 install --upgrade jupyterlab
Use the command below to run locally:
The step above should automatically open a browser window and connect to a running Jupyter server.
If you are using any one of the recommended cloud based deployments (see below), examples are found in the
codeflare/notebooks directory in the container image. The examples can be executed directly from the Jupyter environment.
As a first example of the API usage, see the sample pipeline.
For an example of how CodeFlare Pipelines can be used to scale out common machine learning problems, see the grid search example. It shows how hyperparameter optimization for a reference pipeline can be scaled and accelerated with both task and data parallelism.