guides/model-deployment-options/

[giscus[bot]]

guides/model-deployment-options/

Learn about YOLO11's diverse deployment options to maximize your model's performance. Explore PyTorch, TensorRT, OpenVINO, TF Lite, and more!.

https://docs.ultralytics.com/guides/model-deployment-options/

[UltralyticsAssistant]

👋 Hello, thank you for your interest in Ultralytics 🚀! We recommend visiting our Docs where you can explore the Model Deployment Options Guide to learn about diverse deployment options like TensorRT, OpenVINO, TF Lite, and more.

If this is a 🐛 Bug Report, please provide a minimum reproducible example so we can assist in debugging effectively.

If this is a ❓ Question about custom deployments, please include any relevant information such as logs, error messages, or explicit details about your use case to help us understand your requirements better.

Join the Ultralytics community where it suits you best. For real-time discussions, check out Discord 🎧. For in-depth conversations, visit our Discourse forum. You can also join the conversation on our Subreddit to share and learn from the broader community!

Upgrade

Please ensure you're using the latest ultralytics package to access all the newest features and supported deployment options. Upgrade in a Python>=3.8 environment with PyTorch>=1.8 installed by running:

pip install -U ultralytics

Environments

YOLO supports a variety of verified environments for hassle-free operation, equipped with dependencies like CUDA/CUDNN, Python, and PyTorch:

• Notebooks with free GPU:
• Google Cloud Deep Learning VM: Guide
• Amazon Deep Learning AMI: Guide
• Docker Image: Guide

Status

If this badge is 🟢 green, all Ultralytics CI checks are passing. CI tests ensure our Models and Tasks operate correctly across supported platforms (macOS, Windows, Ubuntu) every 24 hours and on every commit.

Please note that this is an automated response 😊. An Ultralytics engineer will respond soon to continue assisting you!