Dynamic Graph or Static Graph - How Does Your Model Think?

When we build a machine learning model, it doesn’t just “run” - it performs a series of computational operations represented as a graph. But how exactly is this graph constructed - ahead of time or in real-time? This is where the distinction between Dynamic Graph and Static Graph comes in.

Dynamic Graph

The graph is built during runtime, with each step added in real-time. In other words - every time you feed data, the system “redraws” the computational path.

Example:

In PyTorch or JAX (in Eager Execution mode), every operation you write in code is immediately translated into computation. Want to change logic? Add a condition? Just write it - and it works.

Advantages:

• High flexibility - Easy to debug and modify during development.
• Intuitive - The code looks like regular Python.

Disadvantages:

• Less optimization.
• The graph needs to be “built and torn down” each time - less efficient in production.

Static Graph

Here, the graph is built before runtime. The model goes through a “compilation” stage - built once, then runs quickly and repeatedly.

Example:

In TensorFlow 1.x or ONNX, you define all the model layers and their flow in advance. Afterward, you can run the same graph repeatedly with different data.

Advantages:

• High performance - The system can perform optimizations in advance.
• Suitable for inference and production deployment.

Disadvantages:

• Less flexible for changes during runtime.
• Requires a separate “compilation” stage.

When to Use Each?

Scenario	Dynamic Graph	Static Graph
Research & Development	✅ Yes	❌ Less suitable
Production / Inference	❌ Less efficient	✅ Recommended
Writing Convenience	✅ Very	⚠️ Requires pre-definition
Performance	⚠️ Moderate	🚀 Very high

Bottom Line:

Dynamic Graph gives you control and flexibility, while Static Graph gives you speed and efficiency. Advanced systems combine the two - to enjoy the benefits of both.