  Access to vast amounts of data along with affordable computational power
stimulated the reincarnation of neural networks. The progress could not be
achieved without adequate software tools, lowering the entry bar for the next
generations of researchers and developers. The paper introduces PyTorchPipe
(PTP), a framework built on top of PyTorch. Answering the recent needs and
trends in machine learning, PTP facilitates building and training of complex,
multi-modal models combining language and vision (but is not limited to those
two modalities). At its core, PTP employs a component-oriented approach and
relies on the concept of a pipeline, defined as a directed acyclic graph of
loosely coupled components. A user defines a pipeline using yaml-based (thus
human-readable) configuration files, whereas PTP provides generic workers for
their loading, training, and testing using all the computational power (CPUs
and GPUs) that is available to the user. The paper covers the main concepts of
PyTorchPipe, discusses its key features and briefly presents the currently
implemented tasks, models and components.
