This repository contains automation recipes to run native MLOps and DevOps scripts and tools via a simple, common and human-readable CM interface on any platform with any software stack.
All СM scripts have a simple Python API, extensible JSON/YAML meta description and unifed input/output to make them reusable in different projects either individually or by chaining them together into powerful, efficient and portable automation workflows, applications and web services adaptable to continuously changing models, data sets, software and hardware (see CM workflow/pipeline for MLPerf inference as example).
These automation recipes are being developed and maintained by the MLCommons Task Force on Automation and Reproducibility with great contributions from the community and important feedback from Google, AMD, Neural Magic, OctoML, Nvidia, Qualcomm, Dell, HPE, Red Hat, Intel, TTA, One Stop Systems, ACM and other organizations.
See the automatically generated catalog at GitHub or via the Collective Knowledge playground.
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