We present GARFIELD, a library to transparently make machine learning (ML) applications, initially built with popular (but fragile) frameworks, e.g., TensorFlow and PyTorch, Byzantine–resilient. GARFIELD relies on a novel object–oriented design, reducing the coding effort, and addressing the vulnerability of the shared–graph architecture followed by classical ML frameworks. GARFIELD encompasses various communication patterns and supports computations on CPUs and GPUs, allowing addressing the general question of the practical cost of Byzantine resilience in ML applications. We report on the usage of GARFIELD on three main ML architectures: (a) a single server with multiple workers, (b) several servers and workers, and (c) peer–to–peer settings. Using GARFIELD, we highlight interesting facts about the cost of Byzantine resilience. In particular, (a) Byzantine resilience, unlike crash resilience, induces an accuracy loss, (b) the throughput overhead comes more from communication than from robust aggregation, and (c) tolerating Byzantine servers costs more than tolerating Byzantine workers.