Developer Resources For Financial Services

CUDA

CUDA® is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs.

In GPU-accelerated applications, the sequential part of the workload runs on the CPU - which is optimized for single-threaded performance - while the compute intensive portion of the application runs on thousands of GPU cores in parallel.

RAPIDS

The RAPIDS suite of open source software libraries and APIs gives you the ability to execute end-to-end data science and analytics pipelines entirely on GPUs. Licensed under Apache 2.0, RAPIDS is incubated by NVIDIA® based on extensive hardware and data science science experience. RAPIDS utilizes NVIDIA CUDA® primitives for low-level compute optimization, and exposes GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.

Automatic Mixed Precision

Deep Neural Network training has traditionally relied on IEEE single-precision format, however with mixed precision, you can train with half precision while maintaining the network accuracy achieved with single precision. This technique of using both single- and half-precision representations is referred to as mixed precision technique.

Triton Inference Server

The NVIDIA Triton inference server simplifies the deployment of AI models at scale in production. Triton Server is open-source inference server software that lets teams deploy trained AI models from many frameworks, including TensorFlow, TensorRT, PyTorch, and ONNX.

TensorRT

NVIDIA TensorRT™ is a platform for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high-throughput for deep learning inference applications.

cuDNN

The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers.

Jarvis

NVIDIA Jarvis is an SDK for building and deploying AI applications that fuse vision, speech and other sensors. It offers a complete workflow to build, train and deploy GPU-accelerated AI systems that can use visual cues such as gestures and gaze along with speech in context.

JetPack

NVIDIA JetPack SDK is the most comprehensive solution for building AI applications. Use NVIDIA SDK Manager to flash your Jetson developer kit with the latest OS image, install developer tools for both host computer and developer kit, and install the libraries and APIs, samples, and documentation needed to jumpstart your development environment.

NVIDIA Collective Communications Library

The NVIDIA Collective Communications Library (NCCL) implements multi-GPU and multi-node collective communication primitives that are performance optimized for NVIDIA GPUs. NCCL provides routines such as all-gather, all-reduce, broadcast, reduce, reduce-scatter, that are optimized to achieve high bandwidth over PCIe and NVLink high-speed interconnect.

Fundamentals of Accelerated Data Science with RAPIDS

You’ll learn how to:

• Perform multiple analysis tasks on large datasets using RAPIDS
• Use cuDF, Dask, and BlazingSQL to evaluate datasets
• Utilize cuML algorithms to perform data analysis at massive scale

Fundamentals of Accelerated Computing with CUDA Python

You’ll learn how to:

• Use Numba to compile CUDA kernels from NumPy universal functions (ufuncs)
• Use Numba to create and launch custom CUDA kernels
• Apply key GPU memory management techniques

Building Intelligent Recommender Systems

You’ll learn how to:

• Build a content-based recommender system using the open-source cuDF library and Apache Arrow
• Optimize performance for both training and inference using large, sparse datasets
• Deploy a recommender model as a high-performance web service

Accelerating Data Science Workflows with RAPIDS

You’ll learn how to:

• Use cuDF to manipulate massive datasets directly on the GPU
• Utilize a wide variety of GPU-accelerated machine learning algorithms including XGBoost and several other cuML algorithms to perform data analysis
• Perform end to end analysis tasks utilizing several realistic data sets