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tao-run-platform

Submit and monitor GPU training jobs on NVIDIA TAO platforms.

83|20|Updated Apr 16, 2026
Search Tags:#monitoring#sdk#training#nvidia#tao
One-click install
npx skills add https://github.com/NVIDIA-TAO/tao-skill-bank --skill tao-run-platform
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tao-run-platform
Source: https://github.com/NVIDIA-TAO/tao-skill-bank/tree/main/skills/platform/tao-run-platform
Command: npx skills add https://github.com/NVIDIA-TAO/tao-skill-bank --skill tao-run-platform

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires nvidia-tao-sdk, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies the process of submitting and monitoring GPU training jobs on various NVIDIA TAO platforms, providing tools for job tracking, S3 I/O wrapping, and platform-specific features.

Core Features & Use Cases

  • TAO Execution SDK: Provides an optional Python layer for advanced job handling and platform-specific features.
  • Job Tracking and Monitoring: Offers tools to poll job status, stream logs, and manage job handles.
  • S3 I/O Wrapping: Facilitates input download and output upload with S3 integration.
  • Multi-node Training: Supports multi-node distributed training across platforms.
  • Use Case: When you need to run a TAO job with advanced features like job handles, S3 I/O, or platform-specific features not available with docker run.

Quick Start

Use the tao-run-platform skill to submit a GPU training job on the Brev platform.

Frequently Asked Questions about tao-run-platform

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I monitor and track GPU training jobs across NVIDIA TAO platforms?▼

Submit and monitor GPU training jobs using the TAO Execution SDK, which automates job handling and polls job status across Brev, SLURM, local Docker, and Kubernetes platforms.

Does the NVIDIA TAO SDK support multi-node distributed training on Kubernetes?▼

Yes, the NVIDIA TAO SDK supports multi-node distributed training across various platforms including Kubernetes, allowing you to scale advanced GPU training workflows efficiently.

How do I integrate S3 input download and output upload when running TAO jobs?▼

You can integrate S3 I/O wrapping to automatically facilitate input data downloads and output uploads when submitting and managing your GPU training jobs on TAO platforms.

What Python version is required to submit GPU training jobs on NVIDIA TAO platforms?▼

You need Python 3.10 or higher and the nvidia-tao-sdk package installed to submit and monitor GPU training jobs using the advanced job handling features provided.

When should I use the TAO Execution SDK instead of standard docker run for training?▼

Use the TAO Execution SDK when you need advanced features like job handles, S3 I/O wrapping, and platform-specific monitoring capabilities that are not available with a standard docker run command.

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