Get faster deploys and inference with lower costs for YOLOv5
Deploy any YOLOv5 variant on 80+ CPU/GPU targets in AWS, Azure, or GCP in hours, not weeks. OctoML will automatically accelerate your model for the fastest and most cost-efficient inference regardless of hardware target.
OctoML CLI simplifies model deployment with Docker & NVIDIA Triton
The free OctoML Command Line Interface (CLI) packages any of the 10 variants of YOLOv5 into a Docker container with NVIDIA Triton Inference Server to fast-track your object-detection model deployment.
The resulting universal container can be deployed to any Kubernetes infrastructure in any cloud or on-premise environment. Let OctoML handle the heavy-lifting and save hours of time in your deployment workflows.
Download the free OctoML CLI
You built and trained the perfect ML model for your application, now it’s time to push to production. Use the free OctoML CLI to help you get that model deployed to production.
Accelerate YOLOv5 to get the fastest inference on any hardware
Ready to run YOLOv5 in production? Use the OctoML SaaS Platform to accelerate your fine-tuned model via various engines such as Apache TVM, ONNX Runtime, TensorRT and OpenVINO, so that it runs optimally on your target hardware. OctoML supports cloud and edge devices from Intel, NVIDIA, AWS, AMD, ARM, and Qualcomm.
Real-world accelerated inference times
OctoML’s platform shows you your model's before and after acceleration benchmarks across prospective hardware targets. Select the ideal hardware to meet your business objectives and intelligently plan your migration to cloud, CPU to CPU, or GPU to CPU.
Deploy YOLOv5 into production — anywhere
The CLI cleans up the trained model artifact –model math expressed in Python code– and streamlines it into a portable, intelligent function to be used your way.