Releases: deepmodeling/deepmd-kit
Releases · deepmodeling/deepmd-kit
v2.0.0-beta.3
New feature:
- derivatives for deep tensor (#805)
Performance improvement:
- speedup ROCm kernels which use atomicAdd (#809 #815 ) (from ByteDance)
- speedup CUDA kernels (use atomicAdd inside) by reducing the global memory write (#811)
Enhancement:
- add type-embedding developer doc (#762)
- add model compression support for models with exclude_types feature (#754)
- improve the doc and user interface of model compression (#772)
- allow c++ tests to run without internet (#785)
- support converting models generated in v1.3 to 2.0 compatibility (#725)
- give a default value to T and convert models from v1.2 to 2.0 compatibility (#789)
- improved documents for conda (#798)
- throw a message if tf runtime is incompatible (#797)
- capture OOM and print debug message (#801)
Bug fixings
v2.0.0-beta.2
New features:
- Add subcommand and python interface to calculate model-deviation (#715)
Enhancements
- Use fmod to wrap the coord of atoms. UT for force/virial ops (#741)
- UT for model devi C++ interface (#731)
- add CUDA/ROCM buidling documents (#739)
- add op unittests for prod_force, prod_virial, prod_force_grad and prod_virial_grad (#703)
Bug fixings:
v2.0.0-beta.1
v2.0.0-beta.0
Increment to v2.0.0-alpha:
New features:
- Atom type embedding
- Model deviation for virial
Enhancement:
- Improved documentation
- Better support for dipole and polarizability learning
- bit operations to encode neighbor information
- MPI support for atomic model deviation #628
- UT for GPU code #569
- UT for model compression #586
- Test Lammps build #600
Bug fixings
v2.0.0-alpha.1
What's new to v2.0.0-alpha.0
- Training and inference the dipole (vector).
- Split of training and validation dataset.
Enhancement:
- Strict argument check in the input script.
- Update readme for v2.0
- Auto conversion of input file to v2.0 compatibility
Bug fixings:
- Fix bugs of broken examples.
v2.0.0-alpha.0
The very first alpha release of deepmd-kit version 2.0.0. It includes the following new features
- Model compression
- New descriptor: three body embedding
- Hybridization of descriptors
- Long-range modification
- Type embedding (under development)
- Training and inference the dipole (vector) and polarizability (matrix). (under development)
- Split of training and validation dataset. (under development)
- ROCm device support (under development)
Enhancements
- More efficient training: all customized OPs are implemented with GPU.
- Parallel training with multiple GPU support (under development)
Improvement of the code for developers
- Supports version of the model. Easily check model compatability
- Clear and pythonic python interface
- C++ API that can be tested independently
- OP supports multi-device.
Bug fixings:
- remove
using namespace std
. Solves compiling compatability problem. - added
deepmd
namespace for the C++ API