Machine Learning benchmarking with OpenStack and Kubernetes

Deep Learning and Cloud Platforms are transforming the field of Machine Learning from theory to practice. However, implementation differences across frameworks and inference engines make the comparison of benchmark results difficult. SPEC and TPCC benchmarks are not accurate due to the complex interactions between implementation choices such as batch size, hyperparameters, or numerical precision. To address this complexity requires systematic benchmarking that is both representative of real-world use cases