kulkarnihrishikesh / modulus

Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods

Modulus (Beta)

Modulus is an open source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods.

Whether you are exploring the use of Neural operators like Fourier Neural Operators or interested in Physics informed Neural Networks or a hybrid approach in between, Modulus provides you with the optimized stack that will enable you to train your models at real world scale.

This package is the core module that provides the core algorithms, network architectures and utilities that cover a broad spectrum of physics-constrained and data-driven workflows to suit the diversity of use cases in the science and engineering disciplines.

Detailed information on features and capabilities can be found in the Modulus documentation.

Modulus Packages

• Modulus (Beta): Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods.
• Modulus Symbolic (Beta): Framework providing pythonic APIs, algorithms and utilities to be used with Modulus core to physics inform model training as well as higher level abstraction for domain experts.

Domain Specific Packages

• Earth-2 MIP (Beta): Python framework to enable climate researchers and scientists to explore and experiment with AI models for weather and climate.

Installation

PyPi

The recommended method for installing the latest version of Modulus is using PyPi:

pip install nvidia-modulus

The installation can be verified by running a simple python code snippet as shown below:

python
>>> import torch
>>> from modulus.models.mlp.fully_connected import FullyConnected
>>> model = FullyConnected(in_features=32, out_features=64)
>>> input = torch.randn(128, 32)
>>> output = model(input)
>>> output.shape
torch.Size([128, 64])

Optional dependencies

Modulus has many optional dependencies that are used in specific components. When using pip, all dependencies used in Modulus can be installed with pip install nvidia-modulus[all]. If you are developing Modulus, developer dependencies can be installed using pip install nvidia-modulus[dev]. Otherwise, additional dependencies can be installed on a case by case basis. A detailed information on installing the optional dependencies can be found in the Getting Started Guide.

NVCR Container

The recommended Modulus docker image can be pulled from the NVIDIA Container Registry:

docker pull nvcr.io/nvidia/modulus/modulus:24.01

Inside the container you can clone the Modulus git repositories and get started with the examples. Below command show the instructions to launch the modulus container and run an examples from this repo.

docker run --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 --runtime nvidia \
--rm -it nvcr.io/nvidia/modulus/modulus:24.01 bash
git clone https://github.com/NVIDIA/modulus.git
cd modulus/examples/cfd/darcy_fno/
pip install warp-lang # install NVIDIA Warp to run the darcy example
python train_fno_darcy.py

From Source

Package

For a local build of the Modulus Python package from source use:

git clone [email protected]:NVIDIA/modulus.git && cd modulus

pip install --upgrade pip
pip install .

Source Container

To build Modulus docker image:

docker build -t modulus:deploy \
    --build-arg TARGETPLATFORM=linux/amd64 --target deploy -f Dockerfile .

Alternatively, you can run make container-deploy

To build CI image:

docker build -t modulus:ci \
    --build-arg TARGETPLATFORM=linux/amd64 --target ci -f Dockerfile .

Alternatively, you can run make container-ci.

Currently only linux/amd64 and linux/arm64 platforms are supported. If using linux/arm64, some dependencies like warp-lang might not install correctly.

Contributing

Modulus is an open source collaboration and its success is rooted in community contribution to further the field of Physics-ML. Thank you for contributing to the project so others can build on your contribution. For guidance on making a contribution to Modulus, please refer to the contributing guidelines.

Communication

• Github Discussions: Discuss new architectures, implementations, Physics-ML research, etc.
• GitHub Issues: Bug reports, feature requests, install issues, etc.
• Modulus Forum: The Modulus Forum hosts an audience of new to moderate level users and developers for general chat, online discussions, collaboration, etc.

License

Modulus is provided under the Apache License 2.0, please see LICENSE.txt for full license text.