This month the NGC catalog added a new one-click deploy feature, new speech and computer vision models, and sample speech training data to help simplify your AI app development.
The NVIDIA NGC catalog is a hub for GPU-optimized deep learning, machine learning, and HPC applications. With highly performant software containers, pretrained models, industry-specific SDKs, and Jupyter Notebooks the content helps simplify and accelerate end-to-end workflows.
New features, software, and updates to help you streamline your workflow and build your solutions faster on NGC include:
One Click Deploy
Developing AI with your favorite tool, Jupyter Notebooks, just got easier with simplified software deployment using the NGC catalog’s new one-click deploy feature.
Simply go to the software page in the NGC catalog and click on “Deploy to Vertex AI” to get started. Under the hood, this feature: launches the JupyterLab instance on Google Cloud Vertex AI Workbench with optimal configuration; preloads the software dependencies; and downloads the NGC notebook in one go. You can also change the configuration before launching the instance.
Release highlights:
- Jupyter Notebooks for popular AI use-cases.
- One Click Deploy runs NGC Jupyter Notebooks on a Google Cloud Vertex AI Workbench.
- Automated setup with optimal configuration, preloaded dependencies, and ready-to-run notebooks.
- Data scientists can focus on building production-grade models for faster time to market.
See the collection of AI software and Notebooks that you can deploy with one click.
Register for our upcoming webinar to learn how you can use our new feature to build and run your machine learning app 5X faster.
NVIDIA Virtual Machine Image
Virtual Machine Image (VMI) or AMI (in case of AWS) are like operating systems that run on top of the hypervisor on cloud platforms.
NVIDIA GPU-optimized VMI provides a standardized image across IaaS platforms so developers develop their AI application once, whether on NVIDIA-Certified Systems or any GPU cloud instance, and deploy the application on any cloud without code change.
Available from the respective cloud marketplaces, the NVIDIA VMIs are tested on NVIDIA AI software from the NGC catalog to deliver optimized performance and are updated quarterly with the latest drivers, security patches, and support for the latest GPUs.
Organizations may purchase enterprise support of NVIDIA AI software so developers can outsource technical issues and instead focus on building and running AI.
Build your AI today with NVIDIA VMI on AWS, Azure, and Google Cloud.
Deep learning software
The most popular deep learning frameworks for training and inference are updated monthly. Pull the latest version (v22.04) of:
New speech and computer vision models
We are constantly adding state-of-the-art models for a variety of speech and vision models. Here is a list of a handful of new models.
- STT Hi Conformer: Transcribes speech in Hindi characters along with spaces.
- Riva Conformer ASR Spanish: Transcribes speech in lowercase Spanish alphabet.
- EfficientNet v2-S: A family of image classification models, which achieve state-of-the-art accuracy, being an order-of magnitude smaller and faster.
- GatorTron-S: A Megatron BERT model trained on synthetic clinical discharge summaries.
- BioMegatron345m: This NeMo model delivers improved results on a range of biomedical downstream tasks.
To explore more models, visit the NGC Models page.
Sample speech training data
To help you customize pretrained models for your speech application, Defined.AI, an NVIDIA partner, is offering 30 minutes of free sample data for eight languages.
Access it now through the NGC catalog.
HPC Applications
The latest versions of popular HPC applications are also available in the NGC catalog including:
- HPC SDK: A comprehensive suite of compilers, libraries, and HPC tools.
- MATLAB: Provides algorithms, pretrained models, and apps to create, train, visualize, and optimize deep neural networks.
Visit the NGC catalog to see how the GPU-optimized software can help simplify workflows and speed up solution times.