All notable changes to this project will be documented in this file.
The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.
- The citation file.
- Link to the CWA dataset.
- Pangu Weather model
- Fengwu model
- SwinRNN model
- WeatherBench dataset loader
- Consistent handling of single GPU runs in DistributedManager
- Updated DGL build in Dockerfile
- Updated default base image
- Moved Onnx from optional to required dependencies
- Optional Makani dependency required for SFNO model.
- Distributed process group configuration mechanism.
- DistributedManager utility to instantiate process groups based on a process group config.
- Helper functions to faciliate distributed training with shared parameters.
- Brain anomaly detection example.
- Updated Frechet Inception Distance to use Wasserstein 2-norm with improved stability.
- Molecular Dynamics example.
- Improved usage of GraphPartition, added more flexible ways of defining a partitioned graph.
- Physics-Informed Stokes Flow example.
- Profiling markers, benchmarking and performance optimizations for CorrDiff inference.
- MLFLow logging such that only proc 0 logs to MLFlow.
- FNO given seperate methods for constructing lift and spectral encoder layers.
- The experimental SFNO
- Removed experimental SFNO dependencies
- Added CorrDiff dependencies (cftime, einops, pyspng, nvtx)
- Made tqdm a required dependency
- Added Stokes flow dataset
- An experimental version of SFNO to be used in unified training recipe for weather models
- Added distributed FFT utility.
- Added ruff as a linting tool.
- Ported utilities from Modulus Launch to main package.
- EDM diffusion models and recipes for training and sampling.
- NGC model registry download integration into package/filesystem.
- Denoising diffusion tutorial.
- The AFNO input argument
img_size
toinp_shape
- Integrated the network architecture layers from Modulus-Sym.
- Updated the SFNO model, and the training and inference recipes.
- Fixed modulus.Module
from_checkpoint
to work from custom model classes
- Updated the base container to PyTorch 23.10.
- Updated examples to use Pydantic v2.
- Added ability to compute CRPS(..., dim: int = 0).
- Added EFI for arbitrary climatological CDF.
- Added Kernel CRPS implementation (kcrps)
- Added distributed utilities to create process groups and orthogonal process groups.
- Added distributed AFNO model implementation.
- Added distributed utilities for communication of buffers of varying size per rank.
- Added distributed utilities for message passing across multiple GPUs.
- Added instructions for docker build on ARM architecture.
- Added batching support and fix the input time step for the DLWP wrapper.
- Updating file system cache location to modulus folder
- Fixed modulus uninstall in CI docker image
- Handle the tar ball extracts in a safer way.
- Updated the base container to latest PyTorch 23.07.
- Update DGL version.
- Updated require installs for python wheel
- Added optional dependency list for python wheel
- Added a workaround fix for the CUDA graphs error in multi-node runs
- Update
certifi
package version
- Added a CHANGELOG.md
- Added build support for internal DGL
- 4D Fourier Neural Operator model
- Ahmed body dataset
- Unified Climate Datapipe
- DGL install changed from pypi to source
- Updated SFNO to add support for super resolution, flexible checkpoining, etc.
- Fixed issue with torch-harmonics version locking
- Fixed the Modulus editable install
- Fixed AMP bug in static capture
- Fixed security issues with subprocess and urllib in
filesystem.py
- Updated the base container to latest PyTorch base container which is based on torch 2.0
- Container now supports CUDA 12, Python 3.10
- Initial public release.