Merlin is Nvidia's end-to-end recommender system designed for GPU-accelerated work at a production scale.Īs such, it is specialized for recommenders as opposed to, for example, computer vision or NLP text models. Let's take a brief look at Nvidia Merlin, followed by how to run it on Gradient, and the three end-to-end examples provided. Plus others such as Faiss fast similarity search, Protobuf text files, and GraphViz. Nvidia Triton Inference Server deployments into production.Merlin Systems operators and library to help integrate models with other parts of the end-to-end workflow.Merlin Models recommender models including deep learning.generate_data() synthetic data in Merlin.Apache Parquet column-oriented data storage format.Dask open-source Python library for parallel computing.Nvidia RAPIDS cuDF for large dataframes in the familiar Pandas API.Nvidia Merlin NVTabular large-scale ETL workflows, including larger-than-memory datasets, feature engineering, and data preparation. Tools included in the working notebook examples are: The various stages such as data preparation, model training, and deployment, are done on GPU, and thus highly accelerated. Ensemble of models: solve deployment preprocessingĪdditionally, other detail methods such as approximate nearest neighbors search, single-hot and multi-hot encoding of categorical features, and frequency thresholding (less than a given number of occurrences of a class are mapped to same index).Model training of deep learning recommenders.Major parts of end-to-end data science shown in this blog entry are: One of Nvidia's Use Case Frameworks, it has some excellent notebooks containing end-to-end work, and showcases a particularly large range of tools working together to interact with data at scale and provide actionable outputs. Nvidia's GPU-accelerated recommender system, Merlin, is one such example. Therefore, it is of benefit to show working examples of end-to-end data science on Gradient. Gradient is designed to be an end-to-end data science platform, and as such covers all stages of data science, from initial viewing of raw data through to models deployed in production with applications attached.
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