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Production scale, Kubernetes-native vision AI platform, with fully integrated components for model building, automated labeling, data processing and model training pipelines.

• End-to-end workflow and infrastructure automation for production scale vision AI
• Automatic resource management and on-demand scaling of CPU and GPU nodes
• Easily scale your data processing and training pipelines to multiple nodes
• Collaborate on all your deep learning tools and workflows through a unified web interface and SDKs
• Scalability, flexibility and resiliency of Kubernetes without the deployment and configuration complexities

Image and video annotation with automatic annotation Annotate images and video with automatic annotation of bounding boxes and polygon masks, integrated with training pipelines to iteratively improve models for pre-annotation and inference	JupyterLab with TensorFlow, PyTorch and GPU support JupyterLab configured with extensions for debugging, Git/GitHub, notebook diffing and TensorBoard and support for Conda, OpenCV, Tensorflow and PyTorch with GPU and much more
Auto scaling, distributed and parallel data processing and training pipelines Build fully reproducible, distributed and parallel data processing and training pipelines with real-time logs and output snapshots	Version controlled pipelines and environments as code Bring your own IDEs, annotation tools and pipelines with a version controlled YAML and Docker based template engine

• Track and visualize model metrics and experiments with TensorBoard or bring your own experiment tracking tools.
• Access and share tools like AirSim, Carla, Gazebo or OpenAI Gym through your browser with VNC enabled workspaces.
• Extend Onepanel with powerful REST APIs and SDKs to further automate your pipelines and environments.
• Workflows, environments and infrastructure are all defined as code and version controlled, making them reproducible and portable.
• Powered by Kubernetes so you can deploy anywhere Kubernetes can run.

See quick start guide to get started with the platform of your choice.

Getting started with Amazon EKS

See documentation to get started or for more detailed operational and user guides.

To submit a feature request, report a bug or documentation issue, please open a GitHub pull request or issue.

For help, questions, release announcements and contribution discussions, join us on Slack.

Onepanel is modular and consists of the following repositories:

Core API (this repository) - Code base for backend (Go)
Core UI - Code base for UI (Angular + TypeScript)
CLI - Code base for Go CLI for installation and management (Go)
Manifests - Kustomize manifests used by CLI for installation and management (YAML)
Python SDK - Python SDK code and documentation
Templates - Various Workspace, Workflow, Task and Sidecar Templates
Documentation - The repository for documentation site
API Documentation - API documentation if you choose to use the API directly

See CONTRIBUTING.md in each repository for development guidelines. Also, see contribution guide for additional guidelines.

Onepanel seamlessly integrates the following excellent open source projects. We are grateful for the support these communities provide and do our best to contribute back as much as possible.

Argo
CVAT
JupyterLab

Onepanel is licensed under Apache 2.0.

Visit our website for more information about our managed offerings.