Jerry Yan
9988ff00f5
Enable parallel processing for auto-annotation tasks with configurable worker count and file-level parallelism. Key features: - Multi-worker support: WORKER_COUNT env var (default 1) controls number of worker threads - Intra-task file parallelism: FILE_WORKERS env var (default 1) controls concurrent file processing within a single task - Operator chain pooling: Pre-create N independent chain instances to avoid thread-safety issues - Thread-safe progress tracking: Use threading.Lock to protect shared counters - Stop signal handling: threading.Event for graceful cancellation during concurrent processing Implementation details: - Refactor _process_single_task() to use ThreadPoolExecutor + as_completed() - Chain pool (queue.Queue): Each worker thread acquires/releases a chain instance - Protected counters: processed_images, detected_total, file_results with Lock - Stop check: Periodic check of _is_stop_requested() during concurrent processing - Refactor start_auto_annotation_worker(): Move recovery logic here, start WORKER_COUNT threads - Simplify _worker_loop(): Remove recovery call, keep only polling + processing Backward compatibility: - Default config (WORKER_COUNT=1, FILE_WORKERS=1) behaves identically to previous version - No breaking changes to existing deployments Testing: - 11 unit tests all passed: * Multi-worker startup * Chain pool acquire/release * Concurrent file processing * Stop signal handling * Thread-safe counter updates * Backward compatibility (FILE_WORKERS=1) - py_compile syntax check passed Performance benefits: - WORKER_COUNT=3: Process 3 tasks simultaneously - FILE_WORKERS=4: Process 4 files in parallel within each task - Combined: Up to 12x throughput improvement (3 workers × 4 files)
DataMate All-in-One Data Work Platform
DataMate is an enterprise-level data processing platform for model fine-tuning and RAG retrieval, supporting core functions such as data collection, data management, operator marketplace, data cleaning, data synthesis, data annotation, data evaluation, and knowledge generation.
If you like this project, please give it a Star⭐️!
🌟 Core Features
- Core Modules: Data Collection, Data Management, Operator Marketplace, Data Cleaning, Data Synthesis, Data Annotation, Data Evaluation, Knowledge Generation.
- Visual Orchestration: Drag-and-drop data processing workflow design.
- Operator Ecosystem: Rich built-in operators and support for custom operators.
🚀 Quick Start
Prerequisites
- Git (for pulling source code)
- Make (for building and installing)
- Docker (for building images and deploying services)
- Docker-Compose (for service deployment - Docker method)
- Kubernetes (for service deployment - k8s method)
- Helm (for service deployment - k8s method)
This project supports deployment via two methods: docker-compose and helm. After executing the command, please enter the corresponding number for the deployment method. The command echo is as follows:
Choose a deployment method:
1. Docker/Docker-Compose
2. Kubernetes/Helm
Enter choice:
Clone the Code
git clone git@github.com:ModelEngine-Group/DataMate.git
cd DataMate
Deploy the basic services
make install
If the machine you are using does not have make installed, please run the following command to deploy it:
# Windows
set REGISTRY=ghcr.io/modelengine-group/
docker compose -f ./deployment/docker/datamate/docker-compose.yml up -d
docker compose -f ./deployment/docker/milvus/docker-compose.yml up -d
# Linux/Mac
export REGISTRY=ghcr.io/modelengine-group/
docker compose -f ./deployment/docker/datamate/docker-compose.yml up -d
docker compose -f ./deployment/docker/milvus/docker-compose.yml up -d
Once the container is running, access http://localhost:30000 in a browser to view the front-end interface.
To list all available Make targets, flags and help text, run:
make help
Build and deploy Mineru Enhanced PDF Processing
make build-mineru
make install-mineru
Deploy the DeerFlow service
make install-deer-flow
Local Development and Deployment
After modifying the local code, please execute the following commands to build the image and deploy using the local image.
make build
make install dev=true
Uninstall
make uninstall
When running make uninstall, the installer will prompt once whether to delete volumes; that single choice is applied to all components. The uninstall order is: milvus -> label-studio -> datamate, which ensures the datamate network is removed cleanly after services that use it have stopped.
🤝 Contribution Guidelines
Thank you for your interest in this project! We warmly welcome contributions from the community. Whether it's submitting bug reports, suggesting new features, or directly participating in code development, all forms of help make the project better.
• 📮 GitHub Issues: Submit bugs or feature suggestions.
• 🔧 GitHub Pull Requests: Contribute code improvements.
📄 License
DataMate is open source under the MIT license. You are free to use, modify, and distribute the code of this project in compliance with the license terms.