Lambda Stack provides a one line installation and managed upgrade path for: PyTorch, TensorFlow, CUDA, cuDNN, and NVIDIA Drivers. No more futzing with your Linux AI software, Lambda Stack is here. To install Lambda Stack on your desktop, run this command on a fresh Ubuntu installation (20.04, 18.04, or 16.04). For servers, see the server installation section below. Lambda Stack can run on your laptop, workstation, server, cluster, inside a container, on the cloud, and comes pre-installed on every Lambda GPU Cloud instance.

Microsoft HPC continues to invest to deliver the broadest range of Accelerated and high-performance computing (HPC) capabilities in the public cloud. From InfiniBand-enabled Virtual Machine families for artificial intelligence and HPC, to Hyperscale services like Cray supercomputing, Azure enables customers to deliver the full spectrum of AI and machine learning applications. With Azure CycleCloud, we are making it even easier for everyone to deploy, use, and optimize HPC burst, hybrid, or cloud-only clusters. For users running traditional HPC clusters, using schedulers including SLURM, PBS Pro, Grid Engine, LSF, HPC Pack, or HTCondor, this will be the easiest way to get clusters up and running in the cloud, and manage the compute/data workflows, user access, and costs for their HPC workloads over time. With a few clicks, HPC IT administrators can deploy high-performance clusters of compute, storage, filesystem, and application capability in Azure.

New scientific breakthroughs are being made possible by the convergence of HPC and AI. It is now necessary to deploy both HPC and AI workloads on the same system. The complexity of the software environments needed to support HPC and AI workloads is huge. Application software depends on many interdependent software packages. Just getting a successful build can be a challenge, let alone ensuring the build is optimized to take advantage of the very latest hardware and software capabilities.

This repository provides a script and recipe to train the BERT model for PyTorch to achieve state-of-the-art accuracy, and is tested and maintained by NVIDIA. BERT, or Bidirectional Encoder Representations from Transformers, is a new method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. This model is based on the BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding paper. NVIDIA's implementation of BERT is an optimized version of the Hugging Face implementation, leveraging mixed precision arithmetic and Tensor Cores on V100 GPUs for faster training times while maintaining target accuracy. The repository also contains scripts to interactively launch data download, training, benchmarking and inference routines in a Docker container for both pre-training and fine-tuning for tasks such as question answering. This model trains with mixed precision Tensor Cores on Volta and provides a push-button solution to pretraining on a corpus of choice.

This repository provides scripts to train the Jasper model to achieve near state of the art accuracy and perform high-performance inference using NVIDIA TensorRT. This repository is tested and maintained by NVIDIA. The Jasper model is an end-to-end neural acoustic model for automatic speech recognition (ASR) that provides near state-of-the-art results on LibriSpeech among end-to-end ASR models without any external data. The Jasper architecture of convolutional layers was designed to facilitate fast GPU inference, by allowing whole sub-blocks to be fused into a single GPU kernel. This is important for meeting strict real-time requirements of ASR systems in deployment. The results of the acoustic model are combined with the results of external language models to get the top-ranked word sequences corresponding to a given audio segment.

From discovering drugs, to locating black holes, to finding safer nuclear energy sources, high performance computing systems around the world have enabled breakthroughs across all scientific domains. Japan's fastest supercomputer, ABCI, powered by NVIDIA Tensor Core GPUs, enables similar breakthroughs by taking advantage of AI. The system is the world's first large-scale, open AI infrastructure serving researchers, engineers and industrial users to advance their science. The software used to drive these advances is as critical as the servers the software runs on. However, installing an application on an HPC cluster is complex and time consuming.

From discovering drugs, to locating black holes, to finding safer nuclear energy sources, high performance computing systems around the world have enabled breakthroughs across all scientific domains. Japan's fastest supercomputer, ABCI, powered by NVIDIA Tensor Core GPUs, enables similar breakthroughs by taking advantage of AI. The system is the world's first large-scale, open AI infrastructure serving researchers, engineers and industrial users to advance their science. The software used to drive these advances is as critical as the servers the software runs on. However, installing an application on an HPC cluster is complex and time consuming.

As part of our effort to speed the deployment of GPU-accelerated high-performance computing and AI, we've more than tripled the number of containers available from our NVIDIA GPU Cloud (NGC) since launch last year. Users can now take advantage of 35 deep learning, high-performance computing, and visualization containers from NGC, a story we'll be telling in depth at this week's International Supercomputing Conference in Frankfurt. Over the past three years, containers have become a crucial tool in deploying applications on a shared cluster and speeding the work, especially for researchers and data scientists running AI workloads. These containers make deploying deep learning frameworks -- building blocks for designing, training and validating deep neural networks -- faster and easier. Installing frameworks is complicated and time consuming.