Sleep is an evolutionarily conserved behavior, whose function is unknown. Here, we present a method for deep phenotyping of sleep in Drosophila, consisting of a high-resolution video imaging system, coupled with closed-loop laser perturbation to measure arousal threshold. To quantify sleep-associated microbehaviors, we trained a deep-learning network to annotate body parts in freely moving flies and developed a semi-supervised computational pipeline to classify behaviors. Quiescent flies exhibit a rich repertoire of microbehaviors, including proboscis pumping (PP) and haltere switches, which vary dynamically across the night. Using this system, we characterized the effects of optogenetically activating two putative sleep circuits. These data reveal that activating dFB neurons produces micromovements, inconsistent with sleep, while activating R5 neurons triggers PP followed by behavioral quiescence. Our findings suggest that sleep in Drosophila is polyphasic with different stages and set the stage for a rigorous analysis of sleep and other behaviors in this species.