| layout | default |
|---|---|
| title | Deer Flow Tutorial |
| nav_order | 36 |
| has_children | true |
| format_version | v2 |
Orchestrate complex distributed workflows with Deer Flow's powerful task coordination and execution platform.
Deer Flow is increasingly relevant for developers working with modern AI/ML infrastructure. Orchestrate complex distributed workflows with Deer Flow's powerful task coordination and execution platform, and this track helps you understand the architecture, key patterns, and production considerations.
This track focuses on:
- understanding getting started with deer flow
- understanding workflow basics
- understanding task management
- understanding dependencies
Deer Flow is a distributed workflow orchestration platform designed for coordinating complex tasks across multiple systems and services. It provides a robust framework for building, executing, and monitoring distributed workflows with support for parallelism, fault tolerance, and dynamic scaling.
- 🔀 Workflow Orchestration - Complex task coordination and execution
- 📊 Distributed Processing - Scale across multiple nodes and clusters
- 🛡️ Fault Tolerance - Automatic retry and recovery mechanisms
- 📈 Dynamic Scaling - Auto-scale based on workload demands
- 🎯 Task Dependencies - Define complex dependency relationships
- 📊 Monitoring & Observability - Comprehensive workflow monitoring
- 🔌 Extensible Architecture - Custom task types and integrations
- ⏱️ Scheduling - Time-based and event-driven execution
- repository:
bytedance/deer-flow - stars: about 58.4k
graph TB
subgraph "User Interface"
A[Web Dashboard]
B[REST API]
C[CLI Tools]
D[SDK Libraries]
end
subgraph "Orchestration Engine"
E[Workflow Scheduler]
F[Task Coordinator]
G[Dependency Resolver]
H[Execution Engine]
end
subgraph "Execution Layer"
I[Worker Nodes]
J[Task Executors]
K[Resource Manager]
L[Load Balancer]
end
subgraph "Storage Layer"
M[Workflow Definitions]
N[Execution History]
O[Task State]
P[Metrics & Logs]
end
subgraph "Integration Layer"
Q[Message Queues]
R[Databases]
S[External APIs]
T[Cloud Services]
end
A --> E
B --> E
C --> E
D --> E
E --> F
F --> G
G --> H
H --> I
I --> J
J --> K
K --> L
H --> M
H --> N
H --> O
H --> P
F --> Q
F --> R
F --> S
F --> T
| Chapter | Topic | Time | Difficulty |
|---|---|---|---|
| 01-getting-started | Installation & Setup | 20 min | 🟢 Beginner |
| 02-workflow-basics | Basic Workflow Creation | 30 min | 🟢 Beginner |
| 03-task-management | Task Types & Execution | 35 min | 🟡 Intermediate |
| 04-dependencies | Complex Dependencies | 40 min | 🟡 Intermediate |
| 05-error-handling | Fault Tolerance & Recovery | 35 min | 🟡 Intermediate |
| 06-scaling | Distributed Execution | 45 min | 🔴 Expert |
| 07-monitoring | Monitoring & Observability | 30 min | 🔴 Expert |
| 08-advanced-patterns | Advanced Orchestration Patterns | 50 min | 🔴 Expert |
By the end of this tutorial, you'll be able to:
- ✅ Install and configure Deer Flow platform
- ✅ Design and implement complex workflows
- ✅ Manage task dependencies and execution order
- ✅ Implement fault-tolerant workflow patterns
- ✅ Scale workflows across distributed systems
- ✅ Monitor workflow performance and health
- ✅ Integrate with external systems and APIs
- ✅ Optimize workflow performance and reliability
- ✅ Debug and troubleshoot workflow issues
- CPU: 2+ cores recommended
- RAM: 4GB+ recommended
- Storage: 10GB+ for workflow data
- OS: Linux, macOS, Windows
- Docker & Docker Compose
- Python 3.8+
- Node.js 16+ (for web interface)
- Redis or compatible message queue
- Basic programming concepts
- Understanding of distributed systems
- Familiarity with workflow concepts
# Clone repository
git clone https://github.com/bytedance/deer-flow.git
cd deer-flow
# Start with Docker Compose
docker-compose up -d
# Access web interface
open http://localhost:8080
# Submit first workflow
curl -X POST http://localhost:8080/api/workflows \
-H "Content-Type: application/json" \
-d @examples/simple_workflow.json# Create a simple workflow
cat > my_workflow.json << EOF
{
"name": "hello_world",
"tasks": [
{
"id": "task1",
"type": "shell",
"command": "echo 'Hello, Deer Flow!'"
}
]
}
EOF
# Submit workflow
curl -X POST http://localhost:8080/api/workflows \
-H "Content-Type: application/json" \
-d @my_workflow.json- Enterprise-grade workflow orchestration
- Fault tolerance and reliability patterns
- Scalability and performance optimization
- Real-world workflow examples
- Integration patterns and best practices
- Troubleshooting and debugging techniques
- Multi-node deployment strategies
- Load balancing and resource management
- High availability configurations
- Custom task types and integrations
- Plugin architecture for extensions
- API-driven workflow management
- ETL (Extract, Transform, Load) workflows
- Data validation and quality checks
- Batch processing and analytics
- Real-time data streaming
- Order processing and fulfillment
- Customer onboarding workflows
- Approval and review processes
- Notification and communication flows
- Deployment pipelines
- Infrastructure provisioning
- Automated testing and validation
- Rollback and recovery procedures
- Model training pipelines
- Data preprocessing workflows
- Model deployment and serving
- A/B testing and experimentation
Found an issue or want to improve this tutorial? Contributions are welcome!
- Fork this repository
- Create a feature branch
- Make your changes
- Submit a pull request
Special thanks to the ByteDance team for creating this powerful distributed workflow orchestration platform!
Ready to orchestrate distributed workflows? Let's dive into Chapter 1: Getting Started! 🚀
- Start Here: Chapter 1: Getting Started with Deer Flow
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