How to deploy GPU Data Science Workloads on the Cloud

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1. 1 How to deploy GPU Data Science Workloads on the Cloud
2. 2 RAPIDS https://github.com/rapidsai
3. 3 RAPIDS Deployment Models Scales from sharing GPUs to leveraging many GPUs at once Single Node Multi Node Shared Node Scale up interactive data science sessions with NVIDIA accelerated tools like cudf.pandas Scale out processing and training by leveraging GPU acceleration in distributed frameworks like Dask and Spark Scale out AI/ML APIs and model serving with NVIDIA Triton Inference Server and the Forest Inference Library
4. 4 RAPIDS in the Cloud Current Focus Areas • NVIDIA DGX™ Cloud • Kubernetes • Helm Charts • Operator • Kubeflow • Cloud AI/ML Platforms • Amazon Sagemaker Studio • Google Vertex AI • Cloud Compute • Amazon EC2, ECS, Fargate, EKS • Google Compute Engine, Dataproc, GKE • AI and Machine Learning examples gallery RAPIDS Deployment documentation website docs.rapids.ai/deployment/stable
5. 5 RAPIDS on Compute pipelines Data processing services Example from AWS EMR documentation https://docs.nvidia.com/spark-rapids/user-guide/latest/getting-started/aws-emr.html
6. 6 RAPIDS on Managed Notebook Platforms Serverless Jupyter in the cloud Example screenshot from Vertex AI documentation https://docs.rapids.ai/deployment/stable/cloud/gcp/vertex-ai/
7. 7 RAPIDS on Virtual Machines Servers and workstations in the cloud Example from Azure Virtual Machine documentation https://docs.rapids.ai/deployment/stable/cloud/azure/azure-vm/
8. 8 GPU Operator Kubernetes GPU GPU GPU GPU GPU GPU GPU GPU RAPIDS on Kubernetes Unified Cloud Deployments
9. 9 Thank you! Learn more at https://rapids.ai