Tag: TensorFlow

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How To Host A Large Language Model Locally

Large Language Models (LLMs) are a subset of artificial intelligence (AI) that are designed to understand and generate natural language. These models are trained on vast amounts of textual data and use complex algorithms to learn patterns in language. LLMs have been used…

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How Do I Speed Up My Tensorflow Transformer Models?

Transformer models have gained much attention in recent years and have been responsible for many of the advances in Natural Language Processing (NLP). Transformer models have often replaced Recurrent Neural Networks for many use cases like machine translation, text summarization, and…

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Reading And Storing Data For Custom Model Training On Vertex AI

Before you can train ML models in the cloud, you need to get your data to the cloud. But when it comes to storing data on Google Cloud there are a lot of different options. Not to mention the different ways…

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How To Optimize Training Performance With The TensorFlow Profiler On Vertex AI

Training ML models can be computationally expensive. If you’re training models on large datasets, you might be used to model training taking hours, or days, or even weeks. But it’s not just a large volume of data that can increase training…

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Improved TabNet On Vertex AI: High-performance, Scalable Tabular Deep Learning

Data scientists choose models based on various tradeoffs when solving machine learning (ML) problems that involve tabular (i.e., structured) data, the most common data type within enterprises. Among such models, decision trees are popular because they are easy to interpret, fast…

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Scaling Heterogeneous Graph Sampling For GNNs With Google Cloud Dataflow

This blog presents an open-source solution to heterogeneous graph sub-sampling at scale using Google Cloud Dataflow (Dataflow). Dataflow is Google’s publicly available, fully managed environment for running large scale Apache Beam compute pipelines. Dataflow provides monitoring and observability out of the…

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Introducing Model Co-Hosting To Enable Resource Sharing Among Multiple Model Deployments On Vertex AI

When deploying models to the Vertex AI prediction service, each model is by default deployed to its own VM. To make hosting more cost effective, we’re excited to introduce model co-hosting in public preview, which allows you to host multiple models on the same…

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Doing Small Network Scientific Machine Learning In Julia 5x Faster Than PyTorch

Machine learning is a huge discipline, with applications ranging from natural language processing to solving partial differential equations. It is from this landscape that major frameworks such as PyTorch, TensorFlow, and Flux.jl arise and strive to be packages for “all of machine learning”….

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Cloud TPU v4 Records Fastest Training Times On Five MLPerf 2.0 Benchmarks

Today, ML-driven innovation is fundamentally transforming computing, enabling entirely new classes of internet services. For example, recent state-of-the-art lage models such as PaLM and Chinchilla herald a coming paradigm shift where ML services will augment human creativity. All indications are that we are still in…

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New Unified Qualcomm AI Stack Portfolio Revolutionizes Developer Access And Extends AI Leadership Across The Connected Intelligent Edge

Qualcomm Technologies, Inc. announced Qualcomm® AI Stack portfolio, accelerating the company’s leadership in artificial intelligence (AI) and the Connected Intelligent Edge. Combining and improving its best-in-class AI software offerings, Qualcomm AI Stack is a comprehensive AI solution for OEMs and developers,…

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