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Deep Graph Library (DGL) is a framework-neutral, easy-to-use, and scalable Python library used for implementing and training Graph Neural Networks (GNN). Being framework-neutral, DGL is easily integrated into an existing PyTorch, TensorFlow, or an Apache MXNet workflow.

NVIDIA Merlin™ empowers data scientists, machine learning engineers, and researchers to build high-performing recommenders at scale. Merlin includes tools that democratize the building of deep learning recommenders by addressing common extract, transform, load (ETL), training, and inference challenges. Each stage of the pipeline is optimized to support hundreds of terabytes of data, all accessible through easy-to-use APIs. With Merlin, better predictions and increased click-through rates are within reach

NVIDIA NGC is the hub for GPU-optimized software for deep learning, machine learning, and HPC that provides containers, models, model scripts, and industry solutions so data scientists, developers and researchers can focus on building solutions and gathering insights faster.

NVIDIA Merlin™ empowers data scientists, machine learning engineers, and researchers to build high-performing recommenders at scale. Merlin includes tools that democratize the building of deep learning recommenders by addressing common extract, transform, load (ETL), training, and inference challenges. Each stage of the pipeline is optimized to support hundreds of terabytes of data, all accessible through easy-to-use APIs. With Merlin, better predictions and increased click-through rates are within reach

NVIDIA NGC is the hub for GPU-optimized software for deep learning, machine learning, and HPC that provides containers, models, model scripts, and industry solutions so data scientists, developers and researchers can focus on building solutions and gathering insights faster.

Deep Graph Library (DGL) is a framework-neutral, easy-to-use, and scalable Python library used for implementing and training Graph Neural Networks (GNN). Being framework-neutral, DGL is easily integrated into an existing PyTorch, TensorFlow, or an Apache MXNet workflow.