nvmath-python (Beta) is an open-source Python library, providing Python programmers with access to high-performance mathematical operations from NVIDIA CUDA-X math libraries. nvmath-python provides both low-level bindings to the underlying libraries and higher-level Pythonic abstractions. It is interoperable with existing Python packages, such as PyTorch and CuPy. In this post, I show how to��
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The latest release of NVIDIA cuBLAS library, version 12.5, continues to deliver functionality and performance to deep learning (DL) and high-performance computing (HPC) workloads. This post provides an overview of the following updates on cuBLAS matrix multiplications (matmuls) since version 12.0, and a walkthrough: Grouped GEMM APIs can be viewed as a generalization of the batched��
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The NVIDIA CUDA Deep Neural Network library (cuDNN) is a GPU-accelerated library for accelerating deep learning primitives with state-of-the-art performance. cuDNN is integrated with popular deep learning frameworks like PyTorch, TensorFlow, and XLA (Accelerated Linear Algebra). These frameworks abstract the complexities of direct GPU programming, enabling you to focus on designing and��
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A convolutional neural network is a type of deep learning network used primarily to identify and classify images and to recognize objects within images.
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The NVIDIA PyG container, now generally available, packages PyTorch Geometric with accelerations for GNN models, dataloading, and pre-processing using cuGraph-Ops, cuGraph, and cuDF from NVIDIA RAPIDS, all with an effortless out-of-the-box experience.
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With the latest NVIDIA TAO 5.2, you can now run zero-shot inference for panoptic segmentation with ODISE, create custom 3D object pose models, and boost inference throughput for vision transformers using FasterViT. Download now.
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Learn how transformers are used as the building blocks of modern large language models in this new self-paced course.
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Interested in developing LLM-based applications? Get started with this exploration of the open-source ecosystem.
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Take a deep dive into denoising diffusion models, from building a U-Net to training a text-to-image model.
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Discover the power of integrating NVIDIA TAO and Edge Impulse to accelerate AI deployment at the edge.
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Dive into the RAPIDS Accelerator for Apache Spark toolset, including the workload qualification tool for estimating speedup on GPU and the profiling tool for tuning jobs.
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Learn how to train the largest neural networks and deploy them to production.
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Discover how PepsiCo, Runway, SoftServe, and AWS used GPU-accelerated SDKs for their CV applications.
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Deep neural networks (DNNs) are the go-to model for learning functions from data, such as image classifiers or language models. In recent years, deep models have become popular for representing the data samples themselves. For example, a deep model can be trained to represent an image, a 3D object, or a scene, an approach called Implicit Neural Representations. (See also Neural Radiance Fields and��
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Optical Character Detection (OCD) and Optical Character Recognition (OCR) are computer vision techniques used to extract text from images. Use cases vary across industries and include extracting data from scanned documents or forms with handwritten texts, automatically recognizing license plates, sorting boxes or objects in a fulfillment center based on serial numbers��
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NVIDIA Triton Inference Server streamlines and standardizes AI inference by enabling teams to deploy, run, and scale trained ML or DL models from any framework on any GPU- or CPU-based infrastructure. It helps developers deliver high-performance inference across cloud, on-premises, edge, and embedded devices. The nvOCDR library is integrated into Triton for inference.
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Apache Spark is an industry-leading platform for distributed extract, transform, and load (ETL) workloads on large-scale data. However, with the advent of deep learning (DL), many Spark practitioners have sought to add DL models to their data processing pipelines across a variety of use cases like sales predictions, content recommendations, sentiment analysis, and fraud detection. Yet��
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Learn how AI is transforming financial services across use cases such as fraud detection, risk prediction models, contact centers, and more.
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Learn to build and deploy large neural networks to production with this virtual workshop on May 3 from the NVIDIA Deep Learning Institute.
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The Dataiku platform for everyday AI simplifies deep learning. Use cases are far-reaching, from image classification to object detection and natural language processing (NLP). Dataiku helps you with labeling, model training, explainability, model deployment, and centralized management of code and code environments. This post dives into high-level Dataiku and NVIDIA integrations for image��
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