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The rising popularity of deep learning (DL) methods and techniques hasinvigorated interest in the topic of SE4DL (Software Engineering for DeepLearning), the application of software engineering (SE) practices on deeplearning software. Despite the novel engineering challenges brought on by thedata-driven and non-deterministic paradigm of DL software, little work has beeninvested into developing DL-targeted SE tools. On the other hand, toolstackling non-SE issues specific to DL are actively used and referred to underthe umbrella term "MLOps (Machine Learning Operations) tools". Nevertheless,the available literature supports the utility of conventional SE tooling in DLsoftware development. Building upon previous mining software repositories (MSR)research on tool usage in open-source software works, we identify conventionaland MLOps tools adopted in popular applied DL projects that use Python as themain programming language. About 63\% of the GitHub repositories we examinedcontained at least one conventional SE tool. Software construction tools arethe most widely adopted, while the opposite applies to management andmaintenance tools. Relatively few MLOps tools were found to be use, with only20 tools out of a sample of 74 used in at least one repository. The majority ofthem were open-source rather than proprietary. One of these tools, TensorBoard,was found to be adopted in about half of the repositories in our study.Consequently, the widespread use of conventional SE tooling demonstrates itsrelevance to DL software. Further research is recommended on the adoption ofMLOps tooling, focusing on the relevance of particular tool types, thedevelopment of required tools, as well as ways to promote the use of alreadyavailable tools.

Good Tools are Half the Work: Tool Usage in Deep Learning Projects