  Finding a well-performing architecture is often tedious for both DL
practitioners and researchers, leading to tremendous interest in the automation
of this task by means of neural architecture search (NAS). Although the
community has made major strides in developing better NAS methods, the quality
of scientific empirical evaluations in the young field of NAS is still lacking
behind that of other areas of machine learning. To address this issue, we
describe a set of possible issues and ways to avoid them, leading to the NAS
best practices checklist available at http://automl.org/nas_checklist.pdf.
