The instance determines the number of CPU cores, RAM size, and the type and number of GPUs available to the job. All data written to the result is available once the job completes, along with contents of stdout and stderr. Result is a read-write mount specified by the job and captured by the system. Datasets are covered in detail in the Datasets section. See note below.ĭatasets are the data inputs to a job, mounted as read-only to the location specified in the job. Jupyterlab) that are accessed this way is the user’s responsibility. Opening a port when creating a job will create a URL that can be used to reach the container on that port using web protocols. Containers are stored in the NGC Container Registry nvcr.io, accessible from both the CLI and the Web UI. It consists of containers, pre-trained models, Helm charts for Kubernetes deployments, and industry-specific AI toolkits with software development kits (SDKs).Īll applications running in NGC are containerized as Docker containers and execute in our Runtime environment. NGC Catalog is a curated set of GPU-optimized software maintained by NVIDIA and accessible to the general public.

Each ACE has separate storage, compute, and networking. NVIDIA Base Command Platform Terms Īn ACE is a cluster or an availability zone.

The following are a description of common NVIDIA Base Command Platform terms used in this document. NVIDIA Base Command Platform Terms and Concepts 

Reporting and showback capabilities for business leaders who want to measure AI projects against business goals, as well as team managers who need to set project priorities and plan for a successful future by correctly forecasting compute capacity needs.ġ.1. NVIDIA Base Command Platform is a comprehensive platform for businesses, their data scientists, and IT teams, offered in a ready-to-use cloud-hosted solution that manages the end-to-end lifecycle of AI development, AI workflows, and resource management.Ī set of cloud-hosted tools that lets data scientists access the AI infrastructure without interfering with each other.Ī comprehensive cloud-based UI, and a complete command line API to efficiently execute AI workloads with right-sized resources ranging from a single GPU to a multi-node cluster with dataset management, providing quick delivery of production-ready models and applications.Ī built-in telemetry feature to validate deep learning techniques, workload settings, and resource allocations as part of a constant improvement process.

Introduction to NVIDIA Base Command Platform 

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