Reproducibility is an increasing concern in Artificial Intelligence (AI), particularly in the area of Deep Learning (DL). Being able to reproduce DL models is crucial for AI-based systems, as it is closely tied to various tasks like training, testing, debugging, and auditing. However, DL models are challenging to be reproduced due to issues like randomness in the software (e.g., DL algorithms) and non-determinism in the hardware (e.g., GPU). There are various practices to mitigate some of the aforementioned issues. However, many of them are either too intrusive or can only work for a specific usage context. In this paper, we propose a systematic approach to training reproducible DL models. Our approach includes three main parts: (1) a set of general criteria to thoroughly evaluate the reproducibility of DL models for two different domains, (2) a unified framework which leverages a record-and-replay technique to mitigate software-related randomness and a profile-and-patch technique to control hardware-related non-determinism, and (3) a reproducibility guideline which explains the rationales and the mitigation strategies on conducting a reproducible training process for DL models. Case study results show our approach can successfully reproduce six open source and one commercial DL models.

Build verifiability is a safety property for a software system which can be used to check against various security-related issues during the build process. In summary, a verifiable build generates equivalent build artifacts for every build instance, allowing independent auditors to verify that the generated artifacts correspond to their source code. Producing a verifiable build is a very challenging problem, as non-equivalences in the build artifacts can be caused by non-determinsm from the build environment, the build toolchain, or the system implementation. Existing research and practices on build verifiability mainly focus on remediating sources of non-determinism. However, such a process does not work well with large-scale commercial systems (LSCSs) due to their stringent security requirements, complex third party dependencies, and large volumes of code changes. In this paper, we present an experience report on using a unified process and a toolkit to produce verifiable builds for LSCSs. A unified process contrasts with the existing practices in which recommendations to mitigate sources of non-determinism are proposed on a case-by-case basis and are not codified in a comprehensive tool. Our approach supports the following three strategies to systematically mitigate non-equivalences in the build artifacts: remediation, controlling, and interpretation. Case study on three LSCSs within${{\sf Huawei}}$shows that our approach is able to increase the proportion of verified build artifacts from less than 50 to 100 percent. To cross-validate our approach, we successfully applied our approach to build 2,218 open source packages distributed under${{\sf CentOS}}$7.8, increasing the proportion of verified build artifacts from 85 to 99 percent with minimal human intervention. We also provide an overview of our mitigation guideline, which describes the recommended strategies to mitigate various non-equivalences. Finally, we present some discussions and open research problems in this area based on our experience and lessons learned in the past few years of applying our approach within the company. This paper will be useful for practitioners and software engineering researchers who are interested in build verifiability.