Dakkak, Abdul; Li, Cheng

The current landscape of Machine Learning(ML) and Deep Learning(DL) is rife with non-uniform frameworks, models, and system stacks but lacks standard tools to facilitate the evaluation and measurement of model. Due to the absence of such tools, the current practice for evaluating and comparing the benefits of proposed AI innovations (be it hardware or software) on end-to-end AI pipelines is both arduous and error prone --- stifling the adoption of the innovations.

We propose MLModelScope a hardware/software agnostic platform to facilitate the evaluation, measurement, and introspection of ML models within AI pipelines. MLModelScope aids application developers in discovering and experimenting with models, data scientists developers in replicating and evaluating for publishing models, and system architects in understanding the performance of AI workloads.

Machine Learning (ML) and Deep Learning (DL) models are being introduced at a faster pace than researchers are able to analyze and study them. Application builders who may have limited ML knowledge struggle to discover and experiment with state-of-the- art models within their application pipelines. Data scientists find it difficult to reproduce, reuse, or gather unbiased comparison between published models. And, finally, system developers often fail to keep up with current trends, and lag behind in measuring and optimizing frameworks, libraries, and hardware.

We propose MLModelScope, an open source(available on Github at here), extensible, and customizable platform to facilitate evaluation and measurements of ML models within AI pipelines. It is a batteries-included platform for evaluating and profiling ML models across datasets, frameworks, and systems. These evaluations can be used to assess model accuracy and performance across different stacks. We provide an online hub of continuously updated assets, evaluation results, and access to hardware resources allowing users to test and evaluate models without installing or configuring systems. It is framework and hardware agnostic with current support for Caffe, Caffe2, CNTK, MXNet, Tensorflow, and TensorRT running on ARM, PowerPC, and X86 with CPU, GPU, and FPGA. MLModelScopecan be used as an application with a web, command line, or API interface or can be compiled into a standalone library.

More specifically, MLModelScope:

Requires no familiarity with the framework APIs, instead provides a common abstractions that allows programmers to use models.
No coding is needed to publish models, and enables testing of custom software and hardware stacks.
Lowers the cost and effort for performing model analysis and evaluation, making it easier for others to reproduce, evaluate, and analyze the model authors claims.
Makes it simple for system designers to profile and introspect the model and its interaction with the software and hardware stack.

Related papers:

"DLBricks: Composable Benchmark Generation to Reduce Deep Learning Benchmarking Effort on CPUs", Cheng Li, Abdul Dakkak, Jinjun Xiong, Wen-mei Hwu, [more...]
"The Design and Implementation of a Scalable DL Benchmarking Platform", Cheng Li, Abdul Dakkak, Jinjun Xiong, Wen-mei Hwu, [more...]
"Benanza: Automatic Benchmark Generation to Compute "Lower-bound" Latency and Inform Optimizations of Deep Learning Models on GPUs", Cheng Li, Abdul Dakkak, Jinjun Xiong, Wen-mei Hwu, [more...]
"Across-Stack Profiling and Characterization of Machine Learning Models on GPUs", Cheng Li, Abdul Dakkak, Jinjun Xiong, [more...]
"Frustrated with Replicating Claims of a Shared Model? A Solution", Abdul Dakkak, Cheng Li, Jinjun Xiong, [more...]
"Benchmarking and Understanding ML Inference", Cheng Li, Abdul Dakkak, Jinjun Xiong, Wen-mei Hwu, [more...]
"MLModelScope: Evaluate and Measure ML Models within AI Pipelines", Abdul Dakkak, Cheng Li, arXiv preprint arXiv:1811.09737 (2018).. [more...]