The computing demand for massive applications has led to the ubiquitous deployment of
computing power. This trend results in the urgent need for higher-level computing resource …

We present FFCV, a library for easy, fast, resource-efficient training of machine learning
models. FFCV speeds up model training by eliminating (often subtle) data bottlenecks from …

Training Deep Neural Networks (DNNs) is a popular workload in both enterprises and cloud
data centers. Existing schedulers for DNN training consider GPU as the dominant resource …

Nowdays, it is prevalent to train deep learning (DL) models in cloud-native platforms that
actively leverage containerization and orchestration technologies for high elasticity, low and …

Training Deep Learning (DL) model requires multiple resource types, including CPUs,
GPUs, storage IO, and network IO. Advancements in DL have produced a wide spectrum of …

Datacenter-scale AI training clusters consisting of thousands of domain-specific accelerators
(DSA) are used to train increasingly-complex deep learning models. These clusters rely on a …

Training machine learning models requires feeding input data for models to ingest. Input
pipelines for machine learning jobs are often challenging to implement efficiently as they …

Processing input data plays a vital role in ML training, impacting accuracy, throughput, and
cost. The input pipeline, which is responsible for feeding data-hungry GPUs/TPUs with …

Deep Neural Networks (DNNs) have had a significant impact on domains like autonomous
vehicles and smart cities through low-latency inferencing on edge computing devices close …

GPUs are critical for maximizing the throughput-per-Watt of deep neural network (DNN)
applications. However, DNN applications often underutilize GPUs, even when using large …