  The B5G/6G evolution relies on connect-compute technologies and highly
heterogeneous clusters with HW accelerators, which require specialized coding
to be efficiently utilized. The current paper proposes a custom tool for
generating multiple SW versions of a certain AI function input in high-level
language, e.g., Python TensorFlow, while targeting multiple diverse HW+SW
platforms. TF2AIF builds upon disparate tool-flows to create a plethora of
relative containers and enable the system orchestrator to deploy the requested
function on any peculiar node in the cloud-edge continuum, i.e., to leverage
the performance/energy benefits of the underlying HW upon any circumstances.
TF2AIF fills an identified gap in today's ecosystem and facilitates research on
resource management or automated operations, by demanding minimal time or
expertise from users.
