New SDKs Accelerating AI Research, Computer Vision, Data Science, and More

At GTC 2022, NVIDIA revealed major updates to its suite of NVIDIA AI software for developers. The updates accelerate computing in several areas, such as machine…

At GTC 2022, NVIDIA revealed major updates to its suite of NVIDIA AI software for developers. The updates accelerate computing in several areas, such as machine learning research with NVIDIA JAX, AI imaging and computer vision with NVIDIA CV-CUDA, and data science workloads with RAPIDS.

To learn about the latest SDK advancements from NVIDIA, watch the keynote from CEO Jensen Huang.


Just today at GTC 2022, NVIDIA introduced JAX on NVIDIA AI, the newest addition to its GPU-accelerated deep learning frameworks. JAX is a rapidly growing library for high-performance numerical computing and machine learning research.


  • Efficient scaling across multi-node, multi-GPU
  • Easy workflow to train large language models on GPU with GPU-optimized T5X and GPT scripts
  • Built for all major cloud platforms

A ready-to-use JAX container will be available during Q4’2022 in early access. Apply now for early access to JAX and get notified when it is available.

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NVIDIA introduced CV-CUDA, a new open source project enabling developers to build highly efficient, GPU-accelerated pre– and post-processing pipelines in cloud-scale artificial intelligence (AI) imaging and computer vision (CV) workloads.


  • Specialized set of 50+ highly performant CUDA kernels as standalone operators
  • Batching support with variable shape images in one batch

For more CV-CUDA updates, see the CV-CUDA early access interest page.


NVIDIA announced key updates to NVIDIA Triton, open-source, inference-serving software bringing fast and scalable AI to every application in production. Over 50 features were added in the last 12 months.

Notable feature additions:

  • Model orchestration using the NVIDIA Triton Management Service that automates deployment and management of multiple models on Triton Inference Server instances in Kubernetes. Apply for early access.
  • Large language model inference with multi-GPU, multi-node execution with the FasterTransformer backend.
  • Model pipelines (ensembles) with advanced logic using business logic scripting.
  • Auto-generation of minimal required model configuration for fast deployment is on by default.

Kick-start your NVIDIA Triton journey with immediate, short-term access in NVIDIA LaunchPad without setting up your own environment.

You can also download NVIDIA Triton from the NGC catalog, access code and documentation on the /triton-inference-server GitHub repo, and get enterprise-grade support.

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At GTC 2022, NVIDIA announced that RAPIDS, the data science acceleration solution chosen by 25% of Fortune 100 companies, is now further breaking down adoption and usability barriers. It is making accelerated analytics accessible to nearly every organization, whether they’re using low-level C++ libraries, Windows (WSL), or cloud-based data analytics platforms. New capabilities will be available mid-October.


  • Support for WSL and Arm SBSA now generally available
    • Supporting Windows brings the convenience and power of RAPIDS to nine million new Python developers who use Windows.
  • Easily launch multi-node workflows on Kubernetes and Kubeflow
    • Estimating cluster resources in advance for interactive work is often prohibitively challenging. You can now conveniently launch Dask RAPIDS clusters from within your interactive Jupyter sessions and burst beyond the resources of your container for combined ETL and ML workloads.

For more information about the latest release, download and try NVIDIA RAPIDS.

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NVIDIA RAPIDS Accelerator for Apache Spark

New capabilities of the NVIDIA RAPIDS accelerator for Apache Spark 3.x were announced at GTC 2022. The new capabilities bring an unprecedented level of transparency to help you speed up your Apache Spark DataFrame and SQL operations on NVIDIA GPUs, with no code changes and without leaving the Apache Spark environment. Version 22.10 will be available mid-October.

The new capabilities of this release further the mission of accelerating your existing Apache Spark workloads, no matter where you run them.


  • The new workload acceleration tool analyzes Apache Spark workloads and recommends optimized GPU parameters for cost savings and performance.
  • Integration with Google Cloud DataProc.
  • Integration with Delta Lake and Apache Iceberg.

For more information about the latest release, download and try NVIDIA RAPIDS Accelerator for Apache Spark

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PyTorch Geometric and DGL on NVIDIA AI

At GTC 2022, NVIDIA introduced GPU-optimized graph neural network (GNN) frameworks designed to help developers, researchers, and data scientists working on graph learning, including large heterogeneous graphs with billions of edges. With NVIDIA AI-accelerated GNN frameworks, you can achieve end-to-end performance optimization, making it the fastest solution to preprocess and build GNNs.


  • Ready-to-use containers for GPU-optimized PyTorch Geometric and Deep Graph Library
  • Up to 90% lower end-to-end execution time compared to CPUs for ETL, sampling, and training
  • End-to-end reference examples for GraphSage, R-GCN, and SE3-Transformer

For more information about NVIDIA AI-accelerated PyTorch Geometric and DGL and its availability, see the GNN Frameworks page.

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At GTC 2022, NVIDIA announced the latest version of the NVIDIA cuQuantum SDK for accelerating quantum circuit simulation. cuQuantum enables the quantum computing ecosystem to solve problems at the scale of future quantum advantage, enabling the development of algorithms and the design and validation of quantum hardware. 

NVIDIA also announced ecosystem updates for NVIDIA QODA, an open, QPU-agnostic platform for hybrid quantum-classical computing. This hybrid, quantum/classical programming model is interoperable with today’s most important scientific computing applications. We are opening up the programming of quantum computers to a massive new class of domain scientists and researchers.

cuQuantum highlights:

  • Multi-node, multi-GPU support in the DGX cuQuantum appliance
  • Support for approximate tensor network methods
  • Adoption of cuQuantum continues to gain momentum, including CSPs and industrial quantum groups

QODA private beta highlights:

  • Single-source C++ and Python implementations as well as a compiler toolchain for hybrid systems and a standard library of quantum algorithmic primitives
  • QPU-agnostic, partnering with quantum hardware companies across a broad range of qubit modalities
  • Delivering up to a 300X speedup over a leading Pythonic framework also running on an A100 GPU

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