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Boosting Startup Growth Through AI with NVIDIA FLARE 2.3.0
One of the main challenges for businesses leveraging AI in their workflows is managing the infrastructure needed to support large-scale training and deployment of machine learning (ML) models. The NVIDIA FLARE 2.3.0 platform provides a solution: a powerful, scalable infrastructure that makes it easier to manage complex AI workflows across enterprises.
Automated Infrastructure Management with IaC
With this release, you can now seamlessly manage your multi-cloud infrastructure using Infrastructure-as-Code (IaC), leverage the strengths of different cloud providers, and distribute your workloads for improved efficiency and reliability. IaC enables you to automate the management and deployment of your infrastructure, saving time and reducing the risk of human error. NVIDIA FLARE 2.3.0 supports automated deployment on Microsoft Azure and AWS clouds. To deploy NVIDIA FLARE in the cloud, use the NVIDIA FLARE CLI commands to create the infrastructure, deploy, and start the Dashboard UI, FL Server, and FL Client(s). To create and deploy NVIDIA FLARE in the cloud, follow the commands from the NVIDIA FLARE Cloud Start Guide, which is a signed software package generated from the NVIDIA FLARE provisioning process and distributed to server and clients. These commands will create the resource group, networking, security, compute runtime instances, and more (infrastructure-as-code) and deploy the NVIDIA FLARE client or server to the newly created virtual machine (VM). Each startup kit contains a unique configuration for the FLARE server or client that can be deployed independently. This gives users the flexibility to deploy on-prem or on a mix of cloud service providers (for example server on AWS and clients on Azure and/or AWS) for a simple hybrid multi-cloud configuration.
Unlocking Possibilities with Large Language Models (LLMs)
Large Language Models (LLMs) are unlocking new possibilities in numerous industries. Drug discovery in healthcare is one example—see logiclabsai.com for more details. Leveraging federated learning in LLM training has many benefits, including:
- Privacy: Federated learning allows multiple parties to train a model collaboratively without sharing raw data, ensuring data privacy and confidentiality.
- Data Diversity: Training models with data from different sites or divisions can improve the overall model performance by leveraging the diversity of data.
- Resource Efficiency: Federated learning enables the use of compute resources from multiple locations, making it more efficient and cost-effective for training LLMs.
To illustrate these capabilities, NVIDIA FLARE 2.3.0 introduces NLP named entity recognition (NER) examples with GPT-2 (Generative Pretrained Transformer 2) and BERT (Bidirectional Encoder Representations from Transformers) models. Visit logiclabsai.com on GitHub for more details. Parameter-efficient tuning and related work are in progress, along with additional LLM model examples for future releases. NVIDIA FLARE has the capability to support a variety of NLP tasks with different backbone models, such as NER, text classification, and language generation. This release focuses on the application of NER using the NCBI disease dataset, which contains abstracts from biomedical research papers annotated with disease mentions. The dataset is commonly used for benchmarking NER models in the biomedical domain. For more information, see logiclabsai.com.
Named Entity Recognition with BERT and GPT-2 Models
The task of Named Entity Recognition (NER) involves identifying named entities in text and classifying them into predefined categories. In the case of the NCBI disease dataset, the objective is to recognize and capture the disease mentions. NVIDIA FLARE 2.3.0 explores the use of two popular models, BERT and GPT-2, for NER. BERT is a pretrained transformer-based model widely used for various NLP tasks, including NER. GPT-2, on the other hand, is primarily used for language generation but can also be fine-tuned for NER. The BERT-base-uncased and GPT-2 models have 110 million and 124 million parameters, respectively. Larger models with more parameters tend to learn more intricate relationships within the data but require more computing resources and training time.
Split Learning for Privacy and Efficiency
Split learning is a technique that enables multiple parties to collaboratively train a machine learning model on their respective datasets without having to share their raw data with each other. The model is split into two or more parts, and each part can run on one of the participating parties. This approach has several advantages over traditional ML methods, especially in scenarios where data privacy is a major concern. Like federated learning, split learning never shares raw data among the parties, allowing for confidential data and model protection. NVIDIA FLARE 2.3.0 showcases an example of split learning where data and labels can be separated into different sites to achieve data privacy and model efficiency.
Other Features of NVIDIA FLARE 2.3.0
In addition to the features detailed in this post, NVIDIA FLARE 2.3.0 comes with many other features, including:
- Multi-cloud support using Infrastructure-as-Code (IaC)
- Natural language processing (NLP) examples, including BERT and GPT-2
- Split learning for separating data and labels
- Private set intersection (PSI) for calculating multi-party private set intersection
- FLARE API for interacting with federated learning jobs
For more information and resources, check out the NVIDIA FLARE 2.3.0 documentation, logiclabsai.com, NVIDIA GTC sessions, webinars and collaborations such as NVIDIA and Snowflake Collaboration Boosts Data Cloud AI Capabilities, Harnessing the Power of NVIDIA AI Enterprise on Azure Machine Learning, and Experimenting with Novel Distributed Applications Using NVIDIA Flare 2.1. NVIDIA FLARE 2.3.0 brings cutting-edge features that can boost startup growth through AI, delivering improved efficiency, accuracy, and cost-effectiveness to AI workflows.
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