fluidize-python is a library for building modular, reproducible scientific computing pipelines. It provides a unified interface to a wide range of physical simulation tools, eliminating the need to navigate the inconsistent, incomplete instructions that often vary from tool to tool.
This library marks our first step toward AI-orchestrated scientific computing. By standardizing tools and practices within our framework, AI agents can automatically build, configure, and execute computational pipelines across domains and simulation platforms.
Our goal is to improve today’s simulation tools so AI can assist researchers and scientists in accelerating the pace of innovation and scientific discovery.
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Python 3.9+
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Docker Desktop (for local execution). Download and install Docker Desktop from https://docs.docker.com/desktop/.
After installation, verify with:
docker --version
pip install fluidizegit clone https://github.com/Fluidize-Inc/fluidize-python.git
cd fluidize-python
make installExample projects are located in this folder: examples/. There you can find an Jupyter Notebook of a simple simulation
Students and researchers face significant barriers when working with different simulation tools:
- Setup overhead – Installing and configuring someone else’s research code can take an enormous amount of time.
- Diverse architectures – Scientific software is built using a wide range of tools and architectures, each with its own complexities and quirks.
- Time drain – Good software engineering practices are important, but in practice they often slow down the process of getting immediate results.
- Reproducibility issues – Sharing and reproducing experiments is frequently cumbersome and error-prone.
- Scaling friction – Moving from a local prototype to a cloud environment or dedicated compute cluster can be slow and difficult.
Fluidize provides a standardized wrapper that turns complex scientific software into modular components. This makes it possible to:
- Expose a single API endpoint for any scientific computing software—any language, any tool, any complexity.
- Easily connect tools that were never designed to work together.
- Adopt consistent I/O patterns across all simulations.
All of this works with minimal or no changes to the existing codebase, allowing our framework to scale effortlessly to any repository.
At Fluidize, we believe strong organization leads to better reproducibility and scalability.
We treat each simulation pipeline as an individual project. Within projects, each pipeline is treated as a DAG (directed acyclic graph), where nodes represent individual pieces of scientific software (e.g. inputs, solvers, visualization tools, etc.) and edges represent data flow between nodes.
Nodes are the foundational building blocks of simulation pipelines. Each node represents a computational unit with:
| File | Purpose |
|---|---|
properties.yaml |
Container configuration, working directory, and output paths |
metadata.yaml |
Node description, version, authors, and repository URL |
Dockerfile |
Environment setup and dependency installation |
parameters.json |
Tunable parameters for experiments |
main.sh |
Execution script for the source code |
source/ |
Original scientific computing code |
Key Features:
- Predictable input/output paths
- Modular and extensible design
- No source code modification required
- Automated node generation support (Public launch soon)
Projects store a simple data layer for managing individual modules within a pipeline.
| File | Purpose |
|---|---|
graph.json |
Node (scientific software) and edge (data flow) definitions |
metadata.yaml |
Project description and configuration |
Pipelines can be executed both locally and on the cloud. Local execution is handled by Docker engine. Cloud execution is routed through our API, and uses the Kubernetes engine with Argo Workflow Manager.
Comprehensive documentation is available at https://Fluidize-Inc.github.io/fluidize-python/
We would love to collaborate with you! Please see our Contributing Guide for details.
Also - we would love to help streamline your pipeline! Please reach out to us at [email protected].
This is just the beginning of what we believe will be a really exciting new era for how we conduct research and make discoveries in science.
By standardizing tools, we hope to significantly increase the effectiveness of AI in research and discovery. Soon, we will be releasing the following tools built from this framework:
- Auto-Fluidize: Automatically convert any scientific software to run anywhere with our framework.
- Fluidize AI Playground: Explore and build simulation pipelines with natural language.
This project is licensed under the MIT License - see the LICENSE file for details.
