According to the World Health Organization (WHO), 3.6 billion medical imaging tests are performed every year globally to diagnose, monitor, and treat various conditions. Most of these images are stored in a globally recognized standard called DICOM (Digital Imaging and Communications in Medicine). Imaging studies in DICOM format are a combination of unstructured images and structured metadata.
]]>Workloads processing large amounts of data, especially those running on the cloud, will often use an object storage service (S3, Google Cloud Storage, Azure Blob Storage, etc.) as the data source. Object storage services can store and serve massive amounts of data, but getting the best performance can require tailoring your workload to how remote object stores behave. This post is for RAPIDS users…
]]>JSON is a widely adopted format for text-based information working interoperably between systems, most commonly in web applications and large language models (LLMs). While the JSON format is human-readable, it is complex to process with data science and data engineering tools. JSON data often takes the form of newline-delimited JSON Lines (also known as NDJSON) to represent multiple records…
]]>AI has evolved from an experimental curiosity to a driving force within biological research. The convergence of deep learning algorithms, massive omics datasets, and automated laboratory workflows has allowed scientists to tackle problems once thought intractable—from rapid protein structure prediction to generative drug design, increasing the need for AI literacy among scientists.
]]>Learn from researchers, scientists, and industry leaders across a variety of topics including AI, robotics, and Data Science.
]]>As the amount of data available to everyone in the world increases, the ability for a consumer to make informed decisions becomes increasingly difficult. Fortunately, large datasets are a beneficial component for recommendation systems, which can make a sometimes overwhelming decision much easier. Graphs are excellent choices for modeling the relationships inherent in the data that fuel…
]]>NVIDIA and Red Hat have partnered to bring continued improvements to the precompiled NVIDIA Driver introduced in 2020. Last month, NVIDIA announced that the open GPU driver modules will become the default recommended way to enable NVIDIA graphics hardware. Today, NVIDIA announced that Red Hat is now compiling and signing the NVIDIA open GPU kernel modules to further streamline the usage for…
]]>In data science, operational efficiency is key to handling increasingly complex and large datasets. GPU acceleration has become essential for modern workflows, offering significant performance improvements. RAPIDS is a suite of open-source libraries and frameworks developed by NVIDIA, designed to accelerate data science pipelines using GPUs with minimal code changes.
]]>Explore the latest advancements in academia, including advanced research, innovative teaching methods, and the future of learning and technology.
]]>A new study and AI model from researchers at Stanford University is streamlining cancer diagnostics, treatment planning, and prognosis prediction. Named MUSK (Multimodal transformer with Unified maSKed modeling), the research aims to advance precision oncology, tailoring treatment plans to each patient based on their unique medical data. “Multimodal foundation models are a new frontier in…
]]>The latest release of the CUDA Toolkit, version 12.8, continues to push accelerated computing performance in data sciences, AI, scientific computing, and computer graphics and simulation, using the latest NVIDIA CPUs and GPUs. This post highlights some of the new features and enhancements included with this release: CUDA Toolkit 12.8 is the first version of the Toolkit to support…
]]>In the world of Python data science, pandas has long reigned as the go-to library for intuitive data manipulation and analysis. However, as data volumes grow, CPU-bound pandas workflows can become a bottleneck. That’s where cuDF and its pandas accelerator mode, , step in. This mode accelerates operations with GPUs whenever possible, seamlessly falling back to the CPU for unsupported…
]]>JSON is a popular format for text-based data that allows for interoperability between systems in web applications as well as data management. The format has been in existence since the early 2000s and came from the need for communication between web servers and browsers. The standard JSON format consists of key-value pairs that can include nested objects. JSON has grown in usage for storing web…
]]>Evaluating large language models (LLMs) and retrieval-augmented generation (RAG) systems is a complex and nuanced process, reflecting the sophisticated and multifaceted nature of these systems. Unlike traditional machine learning (ML) models, LLMs generate a wide range of diverse and often unpredictable outputs, making standard evaluation metrics insufficient. Key challenges include the…
]]>NVIDIA NIM microservices are model inference containers that can be deployed on Kubernetes. In a production environment, it’s important to understand the compute and memory profile of these microservices to set up a successful autoscaling plan. In this post, we describe how to set up and use Kubernetes Horizontal Pod Autoscaling (HPA) with an NVIDIA NIM for LLMs model to automatically scale…
]]>With as many as 800,000 forgotten oil and gas wells scattered across the US, researchers from Lawrence Berkeley National Laboratory (LBNL), have developed an AI model capable of accurately locating, at scale, wells that may be leaking toxic chemicals and greenhouse gases, like methane, into the environment. The model is designed to identify many of the roughly 3.7M oil and gas wells dug in…
]]>Time series forecasting is a powerful data science technique used to predict future values based on data points from the past Open source Python libraries like skforecast make it easy to run time series forecasts on your data. They allow you to “bring your own” regressor that is compatible with the scikit-learn API, giving you the flexibility to work seamlessly with the model of your choice.
]]>In the webinar on January 28th, you’ll get an inside look of the new GPU engine to learn how Polars’ declarative API and query optimizer enable seamless GPU acceleration.
]]>In the rapidly evolving landscape of artificial intelligence, the quality of the data used for training models is paramount. High-quality data ensures that models are accurate, reliable, and capable of generalizing well across various applications. The recent NVIDIA webinar, Enhance Generative AI Model Accuracy with High-Quality Multimodal Data Processing, dove into the intricacies of data…
]]>Traditional computational drug discovery relies almost exclusively on highly task-specific computational models for hit identification and lead optimization. Adapting these specialized models to new tasks requires substantial time, computational power, and expertise—challenges that grow when researchers simultaneously work across multiple targets or properties.
]]>Designing a therapeutic protein that specifically binds its target in drug discovery is a staggering challenge. Traditional workflows are often a painstaking trial-and-error process—iterating through thousands of candidates, each synthesis and validation round taking months if not years. Considering the average human protein is 430 amino acids long, the number of possible designs translates to…
]]>RAPIDS is a suite of open-source GPU-accelerated data science and AI libraries that are well supported for scale-out with distributed engines like Spark and Dask. Ray is a popular open-source distributed Python framework commonly used to scale AI and machine learning (ML) applications. Ray particularly excels at simplifying and scaling training and inference pipelines and can easily target both…
]]>Approximately 220 teams gathered at the Open Data Science Conference (ODSC) West this year to compete in the NVIDIA hackathon, a 24-hour machine learning (ML) competition. Data scientists and engineers designed models that were evaluated based on accuracy and processing speed. The top three teams walked away with prize packages that included NVIDIA RTX Ada Generation GPUs, Google Colab credits…
]]>Classifier models are specialized in categorizing data into predefined groups or classes, playing a crucial role in optimizing data processing pipelines for fine-tuning and pretraining generative AI models. Their value lies in enhancing data quality by filtering out low-quality or toxic data, ensuring only clean and relevant information feeds downstream processes. Beyond filtering…
]]>RAPIDS 24.12 introduces cuDF packages to PyPI, speeds up aggregations and reading files from AWS S3, enables larger-than-GPU memory queries in the Polars GPU engine, and faster graph neural network (GNN) training on real-world graphs. Starting with the 24.12 release of RAPIDS, CUDA 12 builds of , , , and all of their dependencies are now available on PyPI. As a result…
]]>XGBoost is a machine learning algorithm widely used for tabular data modeling. To expand the XGBoost model from single-site learning to multisite collaborative training, NVIDIA has developed Federated XGBoost, an XGBoost plugin for federation learning. It covers vertical collaboration settings to jointly train XGBoost models across decentralized data sources, as well as horizontal histogram-based…
]]>2024 was another landmark year for developers, researchers, and innovators working with NVIDIA technologies. From groundbreaking developments in AI inference to empowering open-source contributions, these blog posts highlight the breakthroughs that resonated most with our readers. NVIDIA NIM Offers Optimized Inference Microservices for Deploying AI Models at Scale Introduced in…
]]>As data grows in volume, velocity, and complexity, the data science field is booming. There’s an ever-increasing demand for talent and skill sets to help design the best data science solutions. However, the expertise that can help drive these breakthroughs requires students to have a foundation in various tools, programming languages, computing frameworks, and libraries. That’s why the…
]]>Modern classification workflows often require classifying individual records and data points into multiple categories instead of just assigning a single label. Open-source Python libraries like scikit-learn make it easier to build models for these multi-label problems. Several models have built-in support for multi-label datasets, and a simple scikit-learn utility function enables using those…
]]>introduced in a previous post, is a GPU-accelerated library that accelerates pandas to deliver significant performance improvements—up to 50x faster—without requiring any changes to your existing code. As part of the NVIDIA RAPIDS ecosystem, acts as a proxy layer that executes operations on the GPU when possible, and falls back to the CPU (via pandas) when necessary.
]]>Antibodies have become the most prevalent class of therapeutics, primarily due to their ability to target specific antigens, enabling them to treat a wide range of diseases, from cancer to autoimmune disorders. Their specificity reduces the likelihood of off-target effects, making them safer and often more effective than small-molecule drugs for complex conditions. As a result…
]]>As we move towards a more dense computing infrastructure, with more compute, more GPUs, accelerated networking, and so forth—multi-gpu training and analysis grows in popularity. We need tools and also best practices as developers and practitioners move from CPU to GPU clusters. RAPIDS is a suite of open-source GPU-accelerated data science and AI libraries. These libraries can easily scale-out for…
]]>AI models for science are often trained to make predictions about the workings of nature, such as predicting the structure of a biomolecule or the properties of a new solid that can become the next battery material. These tasks require high precision and accuracy. What makes AI for science even more challenging is that highly accurate and precise scientific data is often scarce…
]]>AI has proven to be a force multiplier, helping to create a future where scientists can design entirely new materials, while engineers seamlessly transform these designs into production plans—all without ever setting foot in a lab. As AI continues to redefine the boundaries of innovation, this once elusive vision is now more within reach. Recognizing this paradigm shift…
]]>Python is the most common programming language for data science, machine learning, and numerical computing. It continues to grow in popularity among scientists and researchers. In the Python ecosystem, NumPy is the foundational Python library for performing array-based numerical computations. NumPy’s standard implementation operates on a single CPU core, with only a limited set of operations…
]]>As consumer applications generate more data than ever before, enterprises are turning to causal inference methods for observational data to help shed light on how changes to individual components of their app impact key business metrics. Over the last decade, econometricians have developed a technique called double machine learning that brings the power of machine learning models to causal…
]]>The RAPIDS v24.10 release takes another step forward in bringing accelerated computing to data scientists and developers with a seamless user experience. This blog post highlights the new features including: NetworkX accelerated by RAPIDS cuGraph is now GA in the 24.10 release beginning with NetworkX 3.4. This release adds GPU-accelerated graph creation, a new user experience…
]]>The advent of large language models (LLMs) marks a significant shift in how industries leverage AI to enhance operations and services. By automating routine tasks and streamlining processes, LLMs free up human resources for more strategic endeavors, thus improving overall efficiency and productivity. Training and customizing LLMs for high accuracy is fraught with challenges…
]]>The ability to compare the sequences of multiple related proteins is a foundational task for many life science researchers. This is often done in the form of a multiple sequence alignment (MSA), and the evolutionary information retrieved from these alignments can yield insights into protein structure, function, and evolutionary history. Now, with MMseqs2-GPU, an updated GPU-accelerated…
]]>A new machine-learning algorithm that listens to digital heartbeat data could help veterinarians diagnose murmurs and early-stage heart disease in dogs. Developed by a team of researchers from the University of Cambridge, the study analyzes electronic stethoscope recordings to grade murmur intensity and diagnose the stage of myxomatous mitral valve disease (MMVD)—the most common form of heart…
]]>In our previous blog post, we demonstrated how reusing the key-value (KV) cache by offloading it to CPU memory can accelerate time to first token (TTFT) by up to 14x on x86-based NVIDIA H100 Tensor Core GPUs and 28x on the NVIDIA GH200 Superchip. In this post, we shed light on KV cache reuse techniques and best practices that can drive even further TTFT speedups. LLM models are rapidly…
]]>NVIDIA Parabricks is a scalable genomics analysis software suite that solves omics challenges with accelerated computing and deep learning to unlock new scientific breakthroughs. NVIDIA Parabricks v4.4 introduces new features and functionality including accelerated pangenome graph alignment, as announced at the American Society of Human Genetics (ASHG) national meeting. The core new feature…
]]>NVIDIA AI Workbench is a free development environment manager that streamlines data science, AI, and machine learning (ML) projects on systems of choice. The goal is to provide a frictionless way to create, compute, and collaborate on and across PCs, workstations, data centers, and clouds. The basic user experience is straightforward: This post explores highlights of the October release…
]]>UMAP is a popular dimension reduction algorithm used in fields like bioinformatics, NLP topic modeling, and ML preprocessing. It works by creating a k-nearest neighbors (k-NN) graph, which is known in literature as an all-neighbors graph, to build a fuzzy topological representation of the data, which is used to embed high-dimensional data into lower dimensions. RAPIDS cuML already contained…
]]>Fraud in financial services is a massive problem. According to NASDAQ, in 2023, banks faced $442 billion in projected losses from payments, checks, and credit card fraud. It’s not just about the money, though. Fraud can tarnish a company’s reputation and frustrate customers when legitimate purchases are blocked. This is called a false positive. Unfortunately, these errors happen more often than…
]]>By enabling CUDA kernels to be written in Python similar to how they can be implemented within C++, Numba bridges the gap between the Python ecosystem and the performance of CUDA. However, CUDA C++ developers have access to many libraries that presently have no exposure in Python. These include the CUDA Core Compute Libraries (CCCL), cuRAND, and header-based implementations of numeric types…
]]>Pharmaceutical research demands fast, efficient simulations to predict how molecules interact, speeding up drug discovery. Jiqun Tu, a senior developer technology engineer at NVIDIA, and Ellery Russell, tech lead for the Desmond engine at Schrödinger, explore advanced GPU optimization techniques designed to accelerate molecular dynamics simulations. In this NVIDIA GTC 2024 session…
]]>NetworkX accelerated by NVIDIA cuGraph is a newly released backend co-developed with the NetworkX team. NVIDIA cuGraph provides GPU acceleration for popular graph algorithms such as PageRank, Louvain, and betweenness centrality. Depending on the algorithm and graph size, it can significantly accelerate NetworkX workflows, up to 50x, even 500x over NetworkX on CPU. In this post…
]]>New research from the University of Washington is refining AI weather models using deep learning for more accurate predictions and longer-term forecasts. The study, published in Geophysical Research Letters, shows how adjusting initial atmospheric data enables advanced AI models to extend current forecast limits. As extreme weather becomes increasingly more severe and frequent due to climate…
]]>The rapid evolution of AI models has driven the need for more efficient and scalable inferencing solutions. As organizations strive to harness the power of AI, they face challenges in deploying, managing, and scaling AI inference workloads. NVIDIA NIM and Google Kubernetes Engine (GKE) together offer a powerful solution to address these challenges. NVIDIA has collaborated with Google Cloud to…
]]>Open-source datasets have significantly democratized access to high-quality data, lowering the barriers of entry for developers and researchers to train cutting-edge generative AI models. By providing free access to diverse, high-quality, and well-curated datasets, open-source datasets enable the open-source community to train models at or close to the frontier, facilitating the rapid advancement…
]]>Electric vehicle (EV) charging is getting a jolt with an innovative new AI algorithm that boosts efficiency, reduces cost, and keeps the grid from short-circuiting under pressure. Developed by a team of researchers from the Royal Military College of Canada (RMC), the real-time smart solution optimizes charging schedules for large parking lots, balancing quick charging with energy availability.
]]>The evolution of linear programming (LP) solvers has been marked by significant milestones over the past century, from Simplex to the interior point method (IPM). The introduction of primal-dual linear programming (PDLP) has brought another significant advancement. NVIDIA cuOpt has now implemented PDLP with GPU acceleration. Using cutting-edge algorithms, NVIDIA hardware…
]]>Polars, one of the fastest-growing data analytics tools, has just crossed 9M monthly downloads. As a modern DataFrame library, it is designed for efficiently processing datasets that fit on a single machine, without the overhead and complexity of distributed computing systems that are required for massive-scale workloads. As enterprises grapple with complex data problems—ranging from…
]]>NeMo Curator now supports images, enabling you to process data for training accurate generative AI models.
]]>Learn about accelerating vector search with NVIDIA cuVS and Apache Solr on October 10 at Community Over Code.
]]>Antarctica plays a crucial role in regulating Earth’s climate. Most climate research into the world’s coldest, most windswept continent focuses on the surrounding Southern Ocean’s carbon dioxide absorption, or its vast, sunlight-reflecting glaciers. A group of Australian scientists is taking a different approach. Researchers are diving deep into Antarctic moss beds, using an AI-powered edge…
]]>Join NVIDIA cuOpt engineers at INFORMS 2024 on October 22-23 to learn how to revolutionize accelerated computing.
]]>Join us on October 9 to learn how your applications can benefit from NVIDIA CUDA Python software initiatives.
]]>With the rapid expansion of language models over the past 18 months, hundreds of variants are now available. These include large language models (LLMs), small language models (SLMs), and domain-specific models—many of which are freely accessible for commercial use. For LLMs in particular, the process of fine-tuning with custom datasets has also become increasingly affordable and straightforward.
]]>A groundbreaking drug-repurposing AI model could bring new hope to doctors and patients trying to treat diseases with limited or no existing treatment options. Called TxGNN, this zero-shot tool helps doctors find new uses for existing drugs for conditions that might otherwise go untreated. The study, recently published in Nature Medicine and led by scientists from Harvard University…
]]>Some of Africa’s most resource-constrained farmers are gaining access to on-demand, AI-powered advice through a multimodal chatbot that gives detailed recommendations about how to increase yields or fight common pests and crop diseases. Since February, farmers in the East African nation of Malawi have had access to the chatbot, named UlangiziAI, through WhatsApp on mobile phones.
]]>Modern cyber threats have grown increasingly sophisticated, posing significant risks to federal agencies and critical infrastructure. According to Deloitte, cybersecurity is the top priority for governments and public sectors, highlighting the need to adapt to an increasingly digital world for efficiency and speed. Threat examples include insider threats, supply chain vulnerabilities…
]]>Join this virtual developer day to learn how AI and Machine Learning can revolutionize fraud detection and financial crime prevention.
]]>Today, Polars released a new GPU engine powered by RAPIDS cuDF that accelerates Polars workflows up to 13x on NVIDIA GPUs, allowing data scientists to process hundreds of millions of rows of data in seconds on a single machine. Traditional data processing libraries like pandas are single-threaded and become impractical to use beyond a few million rows of data.
]]>Data loading is a critical aspect of deep learning workflows, whether you’re focused on training or inference. However, it often presents a paradox: the need for a highly convenient solution that is simultaneously customizable. These two goals are notoriously difficult to reconcile. One of the traditional solutions to this problem is to scale out the processing and parallelize the user…
]]>The One Billion Row Challenge is a fun benchmark to showcase basic data processing operations. It was originally launched as a pure-Java competition, and has gathered a community of developers in other languages, including Python, Rust, Go, Swift, and more. The challenge has been useful for many software engineers with an interest in exploring the details of text file reading…
]]>Stephen Jones, a leading expert and distinguished NVIDIA CUDA architect, offers his guidance and insights with a deep dive into the complexities of mapping applications onto massively parallel machines. Going beyond the basics to explore the intricacies of GPU programming, he focuses on practical techniques such as parallel program design and specific details of GPU optimization for improving the…
]]>Domain-adaptive pretraining (DAPT) of large language models (LLMs) is an important step towards building domain-specific models. These models demonstrate greater capabilities in domain-specific tasks compared to their off-the-shelf open or commercial counterparts. Recently, NVIDIA published a paper about ChipNeMo, a family of foundation models that are geared toward industrial chip design…
]]>NetworkX is a popular, easy-to-use Python library for graph analytics. However, its performance and scalability may be unsatisfactory for medium-to-large-sized networks, which can significantly hinder user productivity. NVIDIA and ArangoDB have collectively addressed these performance and scaling issues with a solution that requires zero code changes to NetworkX.
]]>Immerse yourself in NVIDIA technology with our full-day, hands-on technical workshops at our AI Summit in Washington D.C. on October 7, 2024.
]]>The International Society of Automation (ISA) reports that 5% of plant production is lost annually due to downtime. Putting that into a different context, roughly $647B is surrendered on a global basis by manufacturers across all industry segments, the corresponding portion of nearly $13T in production. The challenge at hand is predicting the maintenance needs of these machines to minimize…
]]>Driven by shifts in consumer behavior and the pandemic, e-commerce continues its explosive growth and transformation. As a result, logistics and transportation firms find themselves at the forefront of a parcel delivery revolution. This new reality is especially evident in last-mile delivery, which is now the most expensive element of supply chain logistics. It represents more than 41%
]]>To fully harness the capabilities of NVIDIA GPUs, optimizing NVIDIA CUDA performance is essential, particularly for developers new to GPU programming. This talk is specifically designed for those stepping into the world of CUDA, providing a solid foundation in GPU architecture principles and optimization techniques. Athena Elafrou, a developer technology engineer at NVIDIA…
]]>RAPIDS 24.08 is now available with significant updates geared towards processing larger workloads and seamless CPU/GPU interoperability.
]]>Six years ago, we embarked on a journey to develop an AI inference serving solution specifically designed for high-throughput and time-sensitive production use cases from the ground up. At that time, ML developers were deploying bespoke, framework-specific AI solutions, which were driving up their operational costs and not meeting their latency and throughput service level agreements.
]]>NVIDIA AI Workbench is a free, user-friendly development environment manager that streamlines data science, ML, and AI projects on your system of choice: PC, workstation, datacenter, or cloud. You can develop, test, and prototype projects locally on Windows, macOS, and Ubuntu and easily transfer development environments and computational work between systems (local and remote) to optimize cost…
]]>Recommender systems play a crucial role in personalizing user experiences across various platforms. These systems are designed to predict and suggest items that users are likely to interact with, based on their past behavior and preferences. Building an effective recommender system involves understanding and leveraging huge, complex datasets that capture interactions between users and items.
]]>Hosted by Dell and NVIDIA, demonstrate how AI Workbench can be used to build and deliver apps for a wide range of tasks and workflows.
]]>In the era of generative AI, vector databases have become indispensable for storing and querying high-dimensional data efficiently. However, like all databases, vector databases are vulnerable to a range of attacks, including cyber threats, phishing attempts, and unauthorized access. This vulnerability is particularly concerning considering that these databases often contain sensitive and…
]]>NVIDIA has released RAPIDS cuDF unified memory and text data processing features that help data scientists continue to use pandas when working with larger and text-heavy datasets in demanding workloads. Data scientists can now accelerate these workloads by up to 30x. RAPIDS is a collection of open-source GPU-accelerated data science and AI libraries. cuDF is a Python GPU DataFrame library for…
]]>GPUs are specially designed to crunch through massive amounts of data at high speed. They have a large amount of compute resources, called streaming multiprocessors (SMs), and an array of facilities to keep them fed with data: high bandwidth to memory, sizable data caches, and the capability to switch to other teams of workers (warps) without any overhead if an active team has run out of data.
]]>The release supports GB100 capabilities and new library enhancements to cuBLAS, cuFFT, cuSOLVER, cuSPARSE, as well as the release of Nsight Compute 2024.3.
]]>VISTA-2D is a new foundational model from NVIDIA that can quickly and accurately perform cell segmentation, a fundamental task in cell imaging and spatial omics workflows that is critical to the accuracy of all downstream tasks. The VISTA-2D model uses an image encoder to create image embeddings, which it can then turn into segmentation masks (Figure 1). The embeddings must contain…
]]>In the first part of the series, we presented an overview of the IVF-PQ algorithm and explained how it builds on top of the IVF-Flat algorithm, using the Product Quantization (PQ) technique to compress the index and support larger datasets. In this part two of the IVF-PQ post, we cover the practical aspects of tuning IVF-PQ performance. It’s worth noting again that IVF-PQ uses a lossy…
]]>In this post, we continue the series on accelerating vector search using NVIDIA cuVS. Our previous post in the series introduced IVF-Flat, a fast algorithm for accelerating approximate nearest neighbors (ANN) search on GPUs. We discussed how using an inverted file index (IVF) provides an intuitive way to reduce the complexity of the nearest neighbor search by limiting it to only a small subset of…
]]>Parquet writers provide encoding and compression options that are turned off by default. Enabling these options may provide better lossless compression for your data, but understanding which options to use for your specific use case is critical to making sure they perform as intended. In this post, we explore which encoding and compression options work best for your string data.
]]>Enterprises face significant challenges in making supply chain decisions that maximize profits while adapting quickly to dynamic changes. Optimal supply chain operations rely on advanced analytics and real-time data processing to adapt to rapidly changing conditions and make informed decisions. Optimization is everywhere. To meet customer commitments and minimize risks…
]]>Geneformer is a recently introduced and powerful AI model that learns gene network dynamics and interactions using transfer learning from vast single-cell transcriptome data. This tool enables researchers to make accurate predictions about gene behavior and disease mechanisms even with limited data, accelerating drug target discovery and advancing understanding of complex genetic networks in…
]]>Join NVIDIA at WeAreDevelopers July 17-19 to learn how accelerated computing tools powered by GPUs are shaping the future.
]]>Mathematical optimization is a powerful tool that enables businesses and people to make smarter decisions and reach any number of goals—from improving operational efficiency to reducing costs to increasing customer satisfaction. Many of these are everyday use cases, such as scheduling a flight, pricing a hotel room, choosing a GPS route, routing delivery trucks, and more. However…
]]>As AI models grow in capability and cost of creation, and hold more sensitive or proprietary data, securing them at rest is increasingly important. Organizations are designing policies and tools, often as part of data loss prevention and secure supply chain programs, to protect model weights. While security engineering discussions focus on prevention (How do we prevent X?), detection (Did X…
]]>The rapidly evolving field of generative AI is focused on building neural networks that can create realistic content such as text, images, audio, and synthetic data. Generative AI is revolutionizing multiple industries by enabling rapid creation of content, powering intelligent knowledge assistants, augmenting software development with coding co-pilots, and automating complex tasks across various…
]]>nvmath-python is an open-source Python library that provides high performance access to the core mathematical operations in the NVIDIA Math Libraries. Available now in beta.
]]>K-means is a clustering algorithm—one of the simplest and most popular unsupervised machine learning (ML) algorithms for data scientists.
]]>At NVIDIA GTC 2024, the RAPIDS team demonstrated new features on NVDashboard v0.10 a dashboard that runs on JupyterLab, for monitoring GPU usage to help maximize the efficiency of GPU resources. We are excited to follow that up by announcing that NVDashboard v0.10 is now available to use. This update introduces a host of improvements, including data streaming through WebSockets for…
]]>Featured in Nature, this post delves into how GPUs and other advanced technologies are meeting the computational challenges posed by AI.
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