Deep learning has become the most popular direction in machine learning and artificial intelligence. However, the preparation of training data, as well as model training, are often time-consuming and become the bottleneck of the end-to-end machine learning lifecycle. Reusing models for inferring a dataset can avoid the costs of retraining. However, when there are multiple candidate models, it is challenging to discover the right model for reuse. Although there exist a number of model sharing platforms such as ModelDB, TensorFlow Hub, PyTorch Hub, and DLHub, most of these systems require model uploaders to manually specify the details of each model and model downloaders to screen keyword search results for selecting a model. We are lacking a highly productive model search tool that selects models for deployment without the need for any manual inspection and/or labeled data from the target domain. This paper proposes multiple model search strategies including various similarity-based approaches and non-similarity-based approaches. We design, implement, and evaluate these approaches on multiple model inference scenarios, including activity recognition, image recognition, text classification, natural language processing, and entity matching. The experimental evaluation showed that our proposed asymmetric similarity-based measurement, adaptivity, outperformed symmetric similarity-based measurements and non-similarity-based measurements in most of the workloads.
翻译:深层次学习已成为机器学习和人工智能中最受欢迎的方向,然而,编写培训数据以及示范培训往往耗费时间,成为终端到终端机器学习生命周期的瓶颈。重新使用模型推断数据集可以避免再培训费用。然而,如果有多个候选模型,发现正确的再利用模式则具有挑战性。虽然有一些模式共享平台,如模型DB、TensorFlow Hub、PyTorrch 枢纽和DLHub,但大多数这些系统都需要模型上载器手工指定每个模型和模型下载器的细节,以筛选关键词搜索结果,以选择模型。我们缺乏一个高效的模型搜索工具,在选择模型时无需任何手动检查和/或标注数据,就可选择模型进行部署。本文提出了多种模式搜索战略,包括各种类似基于性和非类似性的方法。我们设计、实施和评价基于多种模型的假设情景的这些方法,包括活动识别、图像识别、文本分类、自然语言处理和实体在最相似的不对称性中进行最相似的调整。