  Efficient large-scale neural network training and inference on commodity CPU
hardware is of immense practical significance in democratizing deep learning
(DL) capabilities. Presently, the process of training massive models consisting
of hundreds of millions to billions of parameters requires the extensive use of
specialized hardware accelerators, such as GPUs, which are only accessible to a
limited number of institutions with considerable financial resources. Moreover,
there is often an alarming carbon footprint associated with training and
deploying these models. In this paper, we take a step towards addressing these
challenges by introducing BOLT, a sparse deep learning library for training
large-scale search and recommendation models on standard CPU hardware. BOLT
provides a flexible, high-level API for constructing models that will be
familiar to users of existing popular DL frameworks. By automatically tuning
specialized hyperparameters, BOLT also abstracts away the algorithmic details
of sparse network training. We evaluate BOLT on a number of information
retrieval tasks including product recommendations, text classification, graph
neural networks, and personalization. We find that our proposed system achieves
competitive performance with state-of-the-art techniques at a fraction of the
cost and energy consumption and an order-of-magnitude faster inference time.
BOLT has also been successfully deployed by multiple businesses to address
critical problems, and we highlight one customer case study in the field of
e-commerce.
