Developing AI with your favorite tool, Jupyter Notebooks, just got easier due to a partnership between NVIDIA and Google Cloud.
The NVIDIA NGC catalog offers GPU-optimized frameworks, SDKs, pretrained AI models, and example notebooks to help you build AI solutions faster. To further speed up your development workflow, a simplified deployment of this software with the NGC catalog’s new one-click deploy feature was released today.
Simply go to the software page in the NGC catalog and click on “Deploy” 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 include
- Hundreds of Jupyter Notebooks for the most popular AI use-cases.
- One click to run 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.
You can deploy frameworks like TensorFlow and PyTorch from the NGC catalog to Google Cloud Vertex AI Workbench with this feature. This will create the instance, load the framework, and create a blank notebook to start your development.
Now, you can start executing your code right away without needing any IT expertise to configure the development environment.
To quickly get started, we offer Jupyter Notebook examples for hundreds of use-cases including:
- Forest Inference LIbrary: Shows the procedure to deploy a XGBoost model in Triton Inference Server with Forest Inference Library (FIL) backend.
- Recommendation: These example notebooks demonstrate how to use NVTabular with TensorFlow, PyTorch, and HugeCTR. Each example provides additional details about the end-to-end workflow, which includes ETL, Training, and Inference.
- Machine Learning: This notebook, using RAPIDS, shows how to ingest data, conduct ETL, perform EDA, train an XGBoost model, and inference using the trained model.
- Question-Answer: This notebook provides a worked example for utilizing the BERT for TensorFlow model scripts.
And this is just the beginning. We will keep adding more examples across use cases and industries to help you accelerate your AI development.
NVIDIA offers an enterprise support option with the purchase of NVIDIA AI Enterprise licenses that provide a single point of contact for AI developers and researchers. The benefits include hybrid-cloud platform support, access to NVIDIA AI experts and training resources, and long-term support for designated software branches. Learn more about NVIDIA AI Enterprise Support.
Getting started resources
- Visit the NGC Catalog and start developing AI with NVIDIA GPU-optimized software on Google Cloud Vertex AI Workbench.
- Browse our collection of Jupyter notebook examples and run it using One-Click Deploy.
- Join NVIDIA at the Google Cloud Data Cloud Summit on April 6 and watch our session on putting data science workflows into production (without losing your mind.)