![]() Select the TensorFlow recommended runtime (TensorFlow 2.9.1 at the time of writing). This is an expensive package to run, so be considerate of the high GPU costs when choosing a machine type and the possibility of OOM error if using a less powerful machine. We used the Ampere A6000 with 48GB of RAM, but others, like the A100, may work. You can see the use of the Merlin GitHub repository, Docker container, and Nvidia NGC Container catalog Docker registry credentials (see below) under Advanced Options.Ĭhoose a GPU machine to run it on. Create NotebookĪfter signing in, create a Project, then a Notebook. Running it on Gradientīecause Merlin is supplied as Docker containers, plus a GitHub repository, it can be immediately run on Gradient using our runtimes, which combine these two components. By running on Gradient, many of the barriers to setting these up and using them, such as obtaining GPU hardware and setting up Docker, are removed. The easiest way to run it is to use Nvidia's provided GPU-enabled Docker containers. Preprocessing of incoming raw data in a deployment is solved by deploying the multiple components of a typical recommender as an ensemble (Triton ensembles).Models are optimized for inference and can be deployed into production in a robust manner (Triton Inference Server).Distributed model training enables better models to be trained faster (HugeCTR). ![]() Real training data can be augmented by synthetic data when needed, an increasingly popular component of AI work (Merlin generate_data()).A feature store is included so that feature engineering is organized, simplified and reusable (Feast).An efficient column-oriented file storage format is used that is much faster than plain text files (Parquet).Large data can be handled in a familiar API without having to add parallelism into the code (cuDF).Data preparation is done on the GPU, giving large speedups (NVTabular).It solves many of the issues that arise when attempting end-to-end data science at scale, for example: Recommenders are nonetheless a common use case in data science, and require their own frameworks to achieve optimal results like any other sub-domain of Deep Learning. 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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