ML systems are composed of complex combinations of algorithmic solutions applied to process large amounts of data, which requires time and computation resources. Implementations of such systems often rely on various external dependencies. Thereby evaluating such systems may end up being a real challenge.

Containers are efficient means to package such systems but do not carry with them hardware passthroughs for programs configured for a specific hardware; Virtual Machines (VM) offer a better way to guarantee this replicability.

In our work, an entrypoint has been designed to allow the execution of the VMs regardless of their implementation. The interfaced VMs have then been coupled with a batch processing orchestrator, that schedules their execution in an OpenStack project.

As a result, we are able to schedule the execution of any pair of system/dataset over a cluster and therefore optimize the allocation of resources. This setup has been successfully used during a NIST evaluation.

The videos from the Summit keynotes are now available!

Search Result

Schedule Matches

Speaker Matches

Registration Opening Soon