New on NGC: SDKs for Large Language Models, Digital Twins, Digital Biology, and More

New SDKs are available in the NGC catalog, a hub of GPU-optimized deep learning, machine learning, and HPC applications. With highly performant software…

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NVIDIA
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Chintan Patel
•5 min read•intermediate•
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AzureGPTLarge Language ModelsOraclePyTorchT5TensorFlowTransformer

Overview

The article discusses the latest SDKs available in the NGC catalog, focusing on tools for Large Language Models (LLMs), digital twins, and digital biology. It highlights the NVIDIA NeMo framework, BioNeMo service, and Omniverse Replicator, which aim to streamline workflows for AI developers and data scientists.

What You'll Learn

1

How to deploy the NVIDIA NeMo framework for training LLMs

2

Why using prompt learning can enhance LLM accuracy

3

How to utilize the NVIDIA BioNeMo service for drug discovery workflows

4

When to apply synthetic data generation using NVIDIA Omniverse Replicator

Prerequisites & Requirements

  • Understanding of deep learning concepts and frameworks
  • Familiarity with NVIDIA NGC catalog and its offerings(optional)

Key Questions Answered

What is the NVIDIA NeMo framework and its capabilities?
The NVIDIA NeMo framework is an end-to-end solution for training and deploying large language models with up to trillions of parameters. It offers automated distributed data processing and supports training models like GPT-3 and T5, making it suitable for various NLP applications.
How does the NVIDIA BioNeMo service aid in drug discovery?
The NVIDIA BioNeMo service provides a unified cloud environment for AI-based drug discovery workflows. It includes protein models like ESM-1 and OpenFold, enabling researchers to predict protein structures and properties, facilitating the development of novel therapeutics.
What are the features of NVIDIA Omniverse Replicator?
NVIDIA Omniverse Replicator is a framework for synthetic data generation that allows technical artists and developers to create custom data pipelines. It is designed to accelerate the training of perception networks by generating physically accurate 3D data.
What new pretrained models are available on NGC?
New pretrained models on NGC include SLU Conformer-Transformer-Large SLURP for intent classification, Riva ASR Korean LM for automatic speech recognition, and TTS De FastPitch HiFi-GAN for text-to-speech synthesis, among others.

Technologies & Tools

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Framework
Nvidia Nemo
Used for training and deploying large language models.
Service
Nvidia Bionemo
Provides a cloud environment for AI-based drug discovery workflows.
Framework
Nvidia Omniverse Replicator
Enables synthetic data generation for training perception networks.
Framework
Pytorch
Popular deep learning framework for training and inference.
Framework
Tensorflow
Widely used deep learning framework for various AI applications.
Service
Nvidia Triton Inference Server
Facilitates model serving and inference at scale.
Library
Tensorrt
Optimizes deep learning models for high-performance inference.

Key Actionable Insights

1
Leverage the NVIDIA NeMo framework to streamline your LLM training process.
Using NeMo can significantly reduce the complexity of training large models, allowing developers to focus on customizing and deploying their applications effectively.
2
Utilize the BioNeMo service for efficient drug discovery without needing extensive IT infrastructure.
This service enables researchers to quickly access advanced protein models, facilitating faster research and development of new therapeutics.
3
Adopt synthetic data generation with NVIDIA Omniverse Replicator to enhance model training.
By generating realistic 3D data, developers can improve the performance of their perception networks, which is crucial for applications in robotics and automation.