We study semi-supervised domain generalization (SSDG), a more realistic problem setting than existing domain generalization research. In particular, SSDG assumes only a few data are labeled from each source domain, along with abundant unlabeled data. Our proposed approach, called StyleMatch, extends FixMatch's two-view consistency learning paradigm in two crucial ways to address SSDG: first, stochastic modeling is applied to the classifier's weights to mitigate overfitting in the scarce labeled data; and second, style augmentation is integrated as a third view into the multi-view consistency learning framework to enhance robustness to domain shift. Two SSDG benchmarks are established where StyleMatch outperforms strong baseline methods developed in relevant areas including domain generalization and semi-supervised learning.