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tao-run-on-local-docker

Execute TAO SDK jobs in Docker containers with NVIDIA GPU acceleration.

83|20|Updated Apr 16, 2026
Search Tags:#Docker#GPU acceleration#job execution#TAO SDK#remote computing
One-click install
npx skills add https://github.com/NVIDIA-TAO/tao-skill-bank --skill tao-run-on-local-docker
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tao-run-on-local-docker
Source: https://github.com/NVIDIA-TAO/tao-skill-bank/tree/main/skills/platform/tao-run-on-local-docker
Command: npx skills add https://github.com/NVIDIA-TAO/tao-skill-bank --skill tao-run-on-local-docker

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires nvidia-container-toolkit, docker, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Streamlines the execution of TAO SDK jobs by leveraging Docker containers with NVIDIA GPU acceleration, enabling seamless local or remote job execution.

Core Features & Use Cases

  • Local Docker Execution: Deploy TAO jobs on the local Docker daemon or a remote host using DOCKER_HOST.
  • Multi-GPU Support: Utilize the full power of the NVIDIA GPU on your system for efficient computations.
  • Remote Docker Mode: Operate Docker daemons on different machines for decentralized execution.
  • Use Case: Ideal for local development and debugging or for running jobs on a separate server with GPU resources.

Quick Start

Run a TAO SDK job container using local Docker with GPU support by executing: docker run --rm --runtime=nvidia --gpus all <image_name> <command>

Frequently Asked Questions about tao-run-on-local-docker

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

FAQPage Schema
How do I run TAO SDK jobs in Docker with NVIDIA GPU acceleration?▼

To run TAO SDK jobs with NVIDIA GPU acceleration, execute Docker containers using the command `docker run --rm --runtime=nvidia --gpus all <image_name> <command>` on your local machine.

Can I execute TAO SDK jobs on a remote Docker host?▼

Yes, you can execute TAO SDK jobs on a remote Docker host by configuring the `DOCKER_HOST` environment variable, allowing decentralized execution on separate servers with GPU resources.

What dependencies are required to run TAO SDK containers with GPU support?▼

Running TAO SDK containers with GPU support requires Docker, an NVIDIA GPU, and the NVIDIA Container Toolkit to be installed and properly configured on your system.

Does TAO SDK Docker execution support multi-GPU configurations?▼

Yes, TAO SDK Docker execution supports multi-GPU configurations, utilizing the full power of the NVIDIA GPU on your system for efficient computations during job management.

When should I use local Docker execution for TAO SDK jobs?▼

Local Docker execution is ideal for local development and debugging, or for running jobs on a separate server with GPU resources without needing direct integration into the host environment.

Why is the NVIDIA Container Toolkit needed for TAO SDK Docker execution?▼

The NVIDIA Container Toolkit is needed to configure the Docker runtime and enable the NVIDIA GPU acceleration required to execute TAO SDK job containers successfully.

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