TensorFlow Archives - Liwaiwai

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 document classification. For organizations, it can be challenging to deploy transformer models in production and perform inference because inference can be expensive, and the implementation can be complex. Recently we announced the public preview for a new runtime that optimizes serving TensorFlow (TF) models on the Vertex AI Prediction service. We are happy to announce that the optimized Tensorflow…

Data

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 you can read in data when designing input pipelines for custom models. Should you use the Cloud Storage API? Copy data directly to the machine where your training job is running? Use the data I/O library of your preferred ML framework? To make things a little easier for you, we’ve outlined some recommendations for reading data in your custom…

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 time. Nonoptimal implementations such as an inefficient input pipeline or low GPU usage can dramatically increase your training time. Making sure your programs are running efficiently and without bottlenecks is key to faster training. And faster training makes for faster iteration to reach your modeling goals. That’s why we’re excited to introduce the TensorFlow Profiler on Vertex AI, and…

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 to train, and can obtain high accuracy quickly from small-scale datasets. On the other hand, deep neural networks offer superior accuracy on larger datasets, as well as the benefits of end-to-end learning, but are black-box and difficult to interpret. TabNet, an interpretable deep learning architecture developed by Google AI, combines the best of both worlds: it is explainable, like simpler…

Programming

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 box and is routinely used to scale production systems to easily handle extreme datasets. This article will present the problem of graph sub-sampling as a pre-processing step for training a Graph Neural Network (GNN) using Tensorflow-GNN (TF-GNN), Google’s open-source GNN library. The following sections will motivate the problem, present an overview of the necessary tools including Docker, Apache Beam, Google Cloud Dataflow, TF-GNN Unigraph format, TF-GNN…

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 VM, resulting in better utilization of memory and computational resources. The number of models you choose to deploy to the same VM will depend on model sizes and traffic patterns, but this feature is particularly useful for scenarios where you have many deployed models with sparse traffic. Understanding the Deployment Resource Pool Co-hosting model support introduces the concept of…

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”. While some of these frameworks have the backing of large companies such as Facebook and Google, the Julia community has relied on the speed and productivity of the Julia programming language itself in order for its open source community to keep up with the pace of development. It is from this aspect which Flux.jl derives its “slimness”: while PyTorch…

Machine Learning

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 the early stages of what will be the next qualitative step function in computing. Realizing this transformation will require democratized and affordable access through cloud computing where the best of compute, networking, storage, and ML can be brought to bear seamlessly on ever larger-scale problem domains. Today’s release of MLPerf™ 2.0 results from the MLCommons® Association highlights the public availability of…

Artificial Intelligence

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, supporting a wide range of intelligent devices with broad AI software access and compatibility. For the first time, a single AI software portfolio works across Qualcomm Technologies-powered devices, spanning the wide range of Connected Intelligent Edge products – including mobile, automotive, XR, compute, IoT, and cloud platforms. Qualcomm AI Stack supports different AI frameworks and popular runtimes including TensorFlow,…

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”. While some of these frameworks have the backing of large companies such as Facebook and Google, the Julia community has relied on the speed and productivity of the Julia programming language itself in order for its open source community to keep up with the pace of development. It is from this aspect which Flux.jl derives its “slimness”: while PyTorch…

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