v1.1.1

PhysicsNeMo (Core) General Release v1.1.1 (patch to v1.1.0)

Fixed

• Fixed an inadvertent change to the deterministic sampler 2nd order correction

v1.1.0

PhysicsNeMo (Core) General Release v1.1.0

Added

• Added ReGen score-based data assimilation example
• General purpose patching API for patch-based diffusion
• New positional embedding selection strategy for CorrDiff SongUNet models
• Added Multi-Storage Client to allow checkpointing to/from Object Storage

Changed

• Simplified CorrDiff config files, updated default values
• Refactored CorrDiff losses and samplers to use the patching API
• Support for non-square images and patches in patch-based diffusion
• ERA5 download example updated to use current file format convention and
  restricts global statistics computation to the training set
• Support for training custom StormCast models and various other improvements for StormCast
• Updated CorrDiff training code to support multiple patch iterations to amortize
  regression cost and usage of torch.compile
• Refactored physicsnemo/models/diffusion/layers.py to optimize data type
  casting workflow, avoiding unnecessary casting under autocast mode
• Refactored Conv2d to enable fusion of conv2d with bias addition
• Refactored GroupNorm, UNetBlock, SongUNet, SongUNetPosEmbd to support usage of
  Apex GroupNorm, fusion of activation with GroupNorm, and AMP workflow.
• Updated SongUNetPosEmbd to avoid unnecessary HtoD Memcpy of pos_embd
• Updated from_checkpoint to accommodate conversion between Apex optimized ckp
  and non-optimized ckp
• Refactored CorrDiff NVTX annotation workflow to be configurable
• Refactored ResidualLoss to support patch-accumlating training for
  amortizing regression costs
• Explicit handling of Warp device for ball query and sdf
• Merged SongUNetPosLtEmb with SongUNetPosEmb, add support for batch>1
• Add lead time embedding support for positional_embedding_selector. Enable
  arbitrary positioning of probabilistic variables
• Enable lead time aware regression without CE loss
• Bumped minimum PyTorch version from 2.0.0 to 2.4.0, to minimize
  support surface for physicsnemo.distributed functionality.

Dependencies

• Made nvidia.dali an optional dependency

v1.0.1

PhysicsNeMo (Core) General Release v1.0.1

Added

• Added version checks to ensure compatibility with older PyTorch for distributed utilities and ShardTensor

Fixed

• EntryPoint error that occured during physicsnemo checkpoint loading

v1.0.0

PhysicsNeMo (Core) General Release v1.0.0

Added

• DoMINO model architecture, datapipe and training recipe
• Added matrix decomposition scheme to improve graph partitioning
• DrivAerML dataset support in FIGConvNet example.
• Retraining recipe for DoMINO from a pretrained model checkpoint
• Prototype support for domain parallelism of using ShardTensor (new).
• Enable DeviceMesh initialization via DistributedManager.
• Added Datacenter CFD use case.
• Add leave-in profiling utilities to physicsnemo, to easily enable torch/python/nsight
  profiling in all aspects of the codebase.

Changed

• Refactored StormCast training example
• Enhancements and bug fixes to DoMINO model and training example
• Enhancement to parameterize DoMINO model with inlet velocity
• Moved non-dimensionaliztion out of domino datapipe to datapipe in domino example
• Updated utils in physicsnemo.launch.logging to avoid unnecessary wandb and mlflow
  imports
• Moved to experiment-based Hydra config in Lagrangian-MGN example
• Make data caching optional in MeshDatapipe
• The use of older importlib_metadata library is removed

Deprecated

• ProcessGroupConfig is tagged for future deprecation in favor of DeviceMesh.

Fixed

• Update pytests to skip when the required dependencies are not present
• Bug in data processing script in domino training example
• Fixed NCCL_ASYNC_ERROR_HANDLING deprecation warning

Dependencies

• Remove the numpy dependency upper bound
• Moved pytz and nvtx to optional
• Update the base image for the Dockerfile
• Introduce Multi-Storage Client (MSC) as an optional dependency.
• Introduce wrapt as an optional dependency, needed when using
  ShardTensor's automatic domain parallelism

v0.9.0

Modulus (core) general release v0.9.0

Added

• FIGConvUNet model and example.
• The Transolver model.
• The XAeroNet model.
• Incoporated CorrDiff-GEFS-HRRR model into CorrDiff, with lead-time aware SongUNet and
  cross entropy loss.

Changed

• Refactored EDMPrecondSRV2 preconditioner and fixed the bug related to the metadata
• Extended the checkpointing utility to store metadata.
• Corrected missing export of loggin function used by transolver model

v0.8.0

Modulus (core) general release v0.8.0

Added

• Graph Transformer processor for GraphCast/GenCast.
• Utility to generate STL from Signed Distance Field.
• Metrics for CAE and CFD domain such as integrals, drag, and turbulence invariances and
  spectrum.
• Added gradient clipping to StaticCapture utilities.
• Bistride Multiscale MeshGraphNet example.

Changed

• Refactored CorrDiff training recipe for improved usability
• Fixed timezone calculation in datapipe cosine zenith utility.

v0.7.0

Modulus (core) general release v0.7.0

Added

• Code logging for CorrDiff via Wandb.
• Augmentation pipeline for CorrDiff.
• Regression output as additional conditioning for CorrDiff.
• Learnable positional embedding for CorrDiff.
• Support for patch-based CorrDiff training and generation (stochastic sampling only)
• Enable CorrDiff multi-gpu generation
• Diffusion model for fluid data super-resolution (CMU contribution).
• The Virtual Foundry GraphNet.
• A synthetic dataloader for global weather prediction models, demonstrated on GraphCast.
• Sorted Empirical CDF CRPS algorithm
• Support for history, cos zenith, and downscaling/upscaling in the ERA5 HDF5 dataloader.
• An example showing how to train a "tensor-parallel" version of GraphCast on a
  Shallow-Water-Equation example.
• 3D UNet
• AeroGraphNet example of training of MeshGraphNet on Ahmed body and DrivAerNet datasets.
• Warp SDF routine
• DLWP HEALPix model
• Pangu Weather model
• Fengwu model
• SwinRNN model
• Modulated AFNO model

Changed

• Raise ModulusUndefinedGroupError when querying undefined process groups
• Changed Indexing error in examples/cfd/swe_nonlinear_pino for modulus loss function
• Safeguarding against uninitialized usage of DistributedManager

Removed

• Remove mlflow from deployment image

Fixed

• Fixed bug in the partitioning logic for distributing graph structures
  intended for distributed message-passing.
• Fixed bugs for corrdiff diffusion training of EDMv1 and EDMv2

Dependencies

• Update DALI to CUDA 12 compatible version.
• Update minimum python version to 3.10

v0.6.0

Modulus (core) general release v0.6.0

Added

• Added citation file
• Link to the CWA dataset
• ClimateDatapipe: an improved datapipe for HDF5/NetCDF4 formatted climate data
• Performance optimizations to CorrDiff
• Physics-Informed Nonlinear Shallow Water Equations example
• Warp neighbor search routine with a minimal example
• Strict option for loading Modulus checkpoints
• Regression only or diffusion only inference for CorrDiff
• Support for organization level model files on NGC file system
• Physics-Informed Magnetohydrodynamics example

Changed

• Updated Ahmed Body and Vortex Shedding examples to use Hydra config
• Added more config options to FCN AFNO example
• Moved posiitonal embedding in CorrDiff from the dataloader to network architecture

Deprecated

• modulus.models.diffusion.preconditioning.EDMPrecondSR. Use EDMPecondSRV2 instead

Removed

• Pickle dependency for CorrDiff

Fixed

• Consistent handling of single GPU runs in DistributedManager
• Output location of objects downloaded with NGC file system
• Bug in scaling the conditional input in CorrDiff deterministic sampler

Dependencies

• Updated DGL build in Dockerfile
• Updated default base image
• Moved Onnx from optional to required dependencies
• Optional Makani dependency required for SFNO model

v0.5.0

Modulus (core) general release v0.5.0

Added

• Distributed process group configuration mechanism.
• DistributedManager utility to instantiate process groups based on a process group config.
• Helper functions to facilitate 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.

Changed

• MLFLow logging such that only proc 0 logs to MLFlow.
• FNO given separate methods for constructing lift and spectral encoder layers.

Removed

• The experimental SFNO

Dependencies

• Removed experimental SFNO dependencies
• Added CorrDiff dependencies (cftime, einops, pyspng)
• Made tqdm a required dependency

v0.4.0

Modulus (core) general release v0.4.0

Added

• 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.
• Added distributed process group configuration mechanism.
• Added DistributedManager utility to instantiate process groups based on thier process group config.

Changed

• The AFNO input argument img_size to inp_shape.
• Integrated the network architecture layers from Modulus-Sym.

Fixed

• Fixed modulus.Module from_checkpoint to work from custom model classes.

Security

• Updated the base container to PyTorch 23.10.