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v3.1.0

11 Jun 06:01
@njzjz njzjz
v3.1.0
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v3.1.0 Latest
Latest

What's Changed

Highlights

DPA3

DPA3 is an advanced interatomic potential leveraging the message-passing architecture. Designed as a large atomic model (LAM), DPA3 is tailored to integrate and simultaneously train on datasets from various disciplines, encompassing diverse chemical and materials systems across different research domains. Its model design ensures exceptional fitting accuracy and robust generalization within and beyond the training domain. Furthermore, DPA3 maintains energy conservation and respects the physical symmetries of the potential energy surface, making it a dependable tool for a wide range of scientific applications.

Refer to examples/water/dpa3/input_torch.json for the training script. After training, the PyTorch model can be converted to the JAX model.

PaddlePaddle backend

The PaddlePaddle backend features a similar Python interface to the PyTorch backend, ensuring compatibility and flexibility in model development. PaddlePaddle has introduced dynamic-to-static functionality and PaddlePaddle JIT compiler (CINN) in DeePMD-kit, which allow for dynamic shapes and higher-order differentiation. The dynamic-to-static functionality automatically captures the user’s dynamic graph code and converts it into a static graph. After conversion, the CINN compiler is used to optimize the computational graph, thereby enhancing the efficiency of model training and inference. In experiments with the DPA-2 model, we achieved approximately a 40% reduction in training time compared to the dynamic graph, effectively improving the model training efficiency.

Breaking changes

  • breaking: enable PyTorch backend for PyPI LAMMPS by @njzjz in #4728

Other new features

All changes in v3.0.1, v3.0.2, and v3.0.3 are included.

Contributors

New Contributors

Full Changelog: v3.0.0...v3.1.0rc0

Contributors

njzjz, RMeli, and 16 other contributors
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v3.1.0rc0

31 May 18:31
@njzjz njzjz
v3.1.0rc0
265d094
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v3.1.0rc0 Pre-release
Pre-release

What's Changed

Highlights

DPA-3

DPA-3 is an advanced interatomic potential leveraging the message-passing architecture. Designed as a large atomic model (LAM), DPA-3 is tailored to integrate and simultaneously train on datasets from various disciplines, encompassing diverse chemical and materials systems across different research domains. Its model design ensures exceptional fitting accuracy and robust generalization within and beyond the training domain. Furthermore, DPA-3 maintains energy conservation and respects the physical symmetries of the potential energy surface, making it a dependable tool for a wide range of scientific applications.

Refer to examples/water/dpa3/input_torch.json for the training script. After training, the PyTorch model can be converted to the JAX model.

PaddlePaddle backend

The PaddlePaddle backend features a similar Python interface to the PyTorch backend, ensuring compatibility and flexibility in model development. PaddlePaddle has introduced dynamic-to-static functionality and PaddlePaddle JIT compiler (CINN) in DeePMD-kit, which allow for dynamic shapes and higher-order differentiation. The dynamic-to-static functionality automatically captures the user’s dynamic graph code and converts it into a static graph. After conversion, the CINN compiler is used to optimize the computational graph, thereby enhancing the efficiency of model training and inference. In experiments with the DPA-2 model, we achieved approximately a 40% reduction in training time compared to the dynamic graph, effectively improving the model training efficiency.

Breaking changes

  • breaking: enable PyTorch backend for PyPI LAMMPS by @njzjz in #4728

Other new features

All changes in v3.0.1, v3.0.2, and v3.0.3 are included.

Contributors

New Contributors

Full Changelog: v3.0.0...v3.1.0rc0

Contributors

njzjz, RMeli, and 15 other contributors
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v3.0.3

23 May 17:27
@njzjz njzjz
v3.0.3
e7eb16e
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v3.0.3

What's Changed

Breaking changes

  • breaking(wheel): bump minimal macos version to 11.0 (#4704)

Bugfixes

  • fix(tf): fix dplr Python inference (#4753)
  • fix: data type of nloc, nall-nloc in the input of border_op (#4653)
  • fix(data): Throw error when data's element is not present in input.json/type_map (#4639)
  • fix(ase): aviod duplicate stress calculation for ase calculator (#4633)
  • fix(pt): improve OOM detection (#4638)
  • fix(tf): always use float64 for the global tensor (#4735)
  • fix(jax): set default_matmul_precision to tensorfloat32 (#4726)
  • fix(jax): fix NaN in sigmoid grad (#4724)
  • fix: fix compatibility with CMake 4.0 (#4680)

CI/CD

  • fix(CI): set CMAKE_POLICY_VERSION_MINIMUM environment variable (#4692)
  • CI: bump PyTorch to 2.7 (#4717)
  • fix(tests): fix tearDownClass and release GPU memory (#4702)
  • fix(CI): upgrade setuptools to fix its compatibility with wheel (#4700)

Full Changelog: v3.0.2...v3.0.3

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v3.1.0a0

30 Mar 01:47
@njzjz njzjz
v3.1.0a0
52f8ece
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v3.1.0a0 Pre-release
Pre-release

What's Changed

Highlights

DPA-3

DPA-3 is an advanced interatomic potential leveraging the message-passing architecture. Designed as a large atomic model (LAM), DPA-3 is tailored to integrate and simultaneously train on datasets from various disciplines, encompassing diverse chemical and materials systems across different research domains. Its model design ensures exceptional fitting accuracy and robust generalization within and beyond the training domain. Furthermore, DPA-3 maintains energy conservation and respects the physical symmetries of the potential energy surface, making it a dependable tool for a wide range of scientific applications.

Refer to examples/water/dpa3/input_torch.json for the training script. After training, the PyTorch model can be converted to the JAX model.

PaddlePaddle backend

The PaddlePaddle backend features a similar Python interface to the PyTorch backend, ensuring compatibility and flexibility in model development. PaddlePaddle has introduced dynamic-to-static functionality and PaddlePaddle JIT compiler (CINN) in DeePMD-kit, which allow for dynamic shapes and higher-order differentiation. The dynamic-to-static functionality automatically captures the user’s dynamic graph code and converts it into a static graph. After conversion, the CINN compiler is used to optimize the computational graph, thereby enhancing the efficiency of model training and inference. In experiments with the DPA-2 model, we achieved approximately a 40% reduction in training time compared to the dynamic graph, effectively improving the model training efficiency.

Other new features

All changes in v3.0.1 and v3.0.2 are included.

Contributors

New Contributors

Full Changelog: v3.0.0...v3.1.0a0

Contributors

njzjz, RMeli, and 15 other contributors
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v3.0.2

02 Mar 03:32
@njzjz njzjz
v3.0.2
70bc6d8
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v3.0.2

What's Changed

This patch version only contains minor features, bug fixes, enhancements, and documentation improvements.

New features

  • feat(tf): support tensor fitting with hybrid descriptor by @njzjz in #4542

Enhancement

Bugfix

Documentation

  • docs: fix the header of the scaling test table by @njzjz in #4507
  • docs: add sphinx.configuration to .readthedocs.yml by @njzjz in #4553
  • docs: add v3 paper citations by @njzjz in #4619
  • docs: add PyTorch Profiler support details to TensorBoard documentation by @caic99 in #4615

CI/CD

  • CI: switch linux_aarch64 to GitHub hosted runners by @njzjz in #4557

New Contributors

Full Changelog: v3.0.1...v3.0.2

Contributors

njzjz, RMeli, and 5 other contributors
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v3.0.1

23 Dec 20:14
@njzjz njzjz
v3.0.1
e5eac4a
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v3.0.1

This patch version only contains bug fixes, enhancements, and documentation improvements.

What's Changed

Enhancements

  • Perf: print summary on rank 0 (#4434)
  • perf: optimize training loop (#4426)
  • chore: refactor training loop (#4435)
  • Perf: remove redundant checks on data integrity (#4433)
  • Perf: use fused Adam optimizer (#4463)

Bug fixes

  • Fix: add model_def_script to ZBL (#4423)
  • fix: add pairtab compression (#4432)
  • fix(tf): pass type_one_side & exclude_types to DPTabulate in se_r (#4446)
  • fix: print dlerror if dlopen fails (#4485)

Documentation

  • chore(pt): update multitask example (#4419)
  • docs: update DPA-2 citation (#4483)
  • docs: update deepmd-gnn URL (#4482)
  • docs: fix a minor typo on the title of install-from-c-library.md (#4484)

Other Changes

Full Changelog: v3.0.0...v3.0.1

Contributors

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v3.0.0

23 Nov 08:10
@njzjz njzjz
v3.0.0
e695a91
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v3.0.0

DeePMD-kit v3: Multiple-backend Framework, DPA-2 Large Atomic Model, and Plugin Mechanisms

After eight months of public tests, we are excited to present the first stable version of DeePMD-kit v3, an advanced version that enables deep potential models with TensorFlow, PyTorch, or JAX backends. Additionally, DeePMD-kit v3 introduces support for the DPA-2 model, a novel architecture optimized for large atomic models. This release enhances plugin mechanisms, making integrating and developing new models easier.

Highlights

Multiple-backend framework: TensorFlow, PyTorch, and JAX support

image

DeePMD-kit v3 adds a versatile, pluggable framework providing consistent training and inference experience across multiple backends. Version 3.0.0 includes:

  • TensorFlow backend: Known for its computational efficiency with a static graph design.
  • PyTorch backend: A dynamic graph backend that simplifies model extension and development.
  • DP backend: Built with NumPy and Array API, a reference backend for development without heavy deep-learning frameworks.
  • JAX backend: Based on the DP backend via Array API, a static graph backend.
Features TensorFlow PyTorch JAX DP
Descriptor local frame ✅
Descriptor se_e2_a ✅ ✅ ✅ ✅
Descriptor se_e2_r ✅ ✅ ✅ ✅
Descriptor se_e3 ✅ ✅ ✅ ✅
Descriptor se_e3_tebd ✅ ✅ ✅
Descriptor DPA1 ✅ ✅ ✅ ✅
Descriptor DPA2 ✅ ✅ ✅
Descriptor Hybrid ✅ ✅ ✅ ✅
Fitting energy ✅ ✅ ✅ ✅
Fitting dipole ✅ ✅ ✅ ✅
Fitting polar ✅ ✅ ✅ ✅
Fitting DOS ✅ ✅ ✅ ✅
Fitting property ✅ ✅ ✅
ZBL ✅ ✅ ✅ ✅
DPLR ✅
DPRc ✅ ✅ ✅ ✅
Spin ✅ ✅ ✅
Gradient calculation ✅ ✅ ✅
Model training ✅ ✅
Model compression ✅ ✅
Python inference ✅ ✅ ✅ ✅
C++ inference ✅ ✅ ✅

Critical features of the multiple-backend framework include the ability to:

  • Train models using different backends with the same training data and input script, allowing backend switching based on your efficiency or convenience needs.
# Training a model using the TensorFlow backend
dp --tf train input.json
dp --tf freeze
dp --tf compress

# Training a model using the PyTorch backend
dp --pt train input.json
dp --pt freeze
dp --pt compress
  • Convert models between backends using dp convert-backend, with backend-specific file extensions (e.g., .pb for TensorFlow and .pth for PyTorch).
# Convert from a TensorFlow model to a PyTorch model
dp convert-backend frozen_model.pb frozen_model.pth
# Convert from a PyTorch model to a TensorFlow model
dp convert-backend frozen_model.pth frozen_model.pb
# Convert from a PyTorch model to a JAX model
dp convert-backend frozen_model.pth frozen_model.savedmodel
# Convert from a PyTorch model to the backend-independent DP format
dp convert-backend frozen_model.pth frozen_model.dp
  • Run inference across backends via interfaces like dp test, Python/C++/C interfaces, or third-party packages (e.g., dpdata, ASE, LAMMPS, AMBER, Gromacs, i-PI, CP2K, OpenMM, ABACUS, etc.).
# In a LAMMPS file:
# run LAMMPS with a TensorFlow backend model
pair_style deepmd frozen_model.pb
# run LAMMPS with a PyTorch backend model
pair_style deepmd frozen_model.pth
# run LAMMPS with a JAX backend model
pair_style deepmd frozen_model.savedmodel
# Calculate model deviation using different models
pair_style deepmd frozen_model.pb frozen_model.pth frozen_model.savedmodel out_file md.out out_freq 100
  • Add a new backend to DeePMD-kit much more quickly if you want to contribute to DeePMD-kit.

DPA-2 model: a large atomic model as a multi-task learner

The DPA-2 model offers a robust architecture for large atomic models (LAM), accurately representing diverse chemical systems for high-quality simulations. In this release, DPA-2 can be trained using the PyTorch backend, supporting both single-task (see examples/water/dpa2) or multi-task (see examples/water_multi_task/pytorch_example) training schemes. DPA-2 is available for Python/C++ inference in the JAX backend.

The DPA-2 descriptor comprises repinit and repformer, as shown below.

DPA-2

The PyTorch backend supports training strategies for large atomic models, including:

  • Parallel training: Train large atomic models on multiple GPUs for efficiency.
torchrun --nproc_per_node=4 --no-python dp --pt train input.json
  • Multi-task training: For large atomic models trained across a broad range of data calculated on different DFT levels with shared descriptors. An example is given in examples/water_multi_task/pytorch_example/input_torch.json.
  • Finetune: Training a pre-train large atomic model on a smaller, task-specific dataset. The PyTorch backend has supported --finetune argument in the dp --pt train command line.

Plugin mechanisms for external models

In version 3.0.0, the plugin capabilities have been implemented to support the development and integration of potential energy models using TensorFlow, PyTorch, or JAX backends, leveraging DeePMD-kit's trainer, loss functions, and interfaces. A plugin example is deepmd-gnn, which supports training the MACE and NequIP models in the DeePMD-kit with the familiar commands.

dp --pt train mace.json
dp --pt freeze
dp --pt test -m frozen_model.pth -s ../data/

image

Other new features

  • Descriptor se_e3_tebd. (#4066)
  • Fitting the property (#3867).
  • New training parameters: max_ckpt_keep (#3441), change_bias_after_training (#3993), and stat_file.
  • New command line interface: dp change-bias (#3993) and dp show (#3796).
  • Support generating JSON schema for integration with VSCode (#3849).
  • The latest LAMMPS version (stable_29Aug2024_update1) is supported. (#4088, #4179)

Breaking changes

  • The deepmodeling conda channel is deprecated. Use the conda-forge channel instead. (#3462, #4385)
  • The offline package and conda packages for CUDA 11 are dropped.
  • Python 3.7 and 3.8 supports are dropped. (#3185, #4185)
  • The minimal versions of deep learning frameworks: TensorFlow 2.7, PyTorch 2.1, JAX 0.4.33, and NumPy 1.21.
  • We require all model files to have the correct filename extension for all interfaces so a corresponding backend can load them. TensorFlow model files must end with .pb extension.
  • Bias is removed by default from type embedding. (#3958)
  • The spin model is refactored, and its usage in the LAMMPS module has been changed. (#3301, #4321)
  • Multi-task training support is removed from the TensorFlow backend. (#3763)
  • The set_prefix key is deprecated. (#3753)
  • dp test now uses all sets for training and test. In previous versions, only the last set is used as the test set in dp test. (#3862)
  • The Python module structure is fully refactored. The old deepmd module was moved to deepmd.tf without other API changes, and deepmd_utils was moved to deepmd without other API changes. (#3177, #3178)
  • Python class DeepTensor (including DeepDiople and DeepPolar) now returns atomic tensor in the dimension of natoms instead of nsel_atoms. (#3390)
  • C++ 11 support is dropped. (#4068)

For other changes, refer to Full Changelog: v2.2.11...v3.0.0rc0

Contributors

The PyTorch backend was developed in the dptech-corp/deepmd-pytorch repository, and then it was fully merged into the deepmd-kit repository in #3180. Contributors to the deepmd-pytorch repository:

Contributors to the deepmd-kit repository:

Read more

Contributors

amcadmus, chazeon, and 29 other contributors
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v3.0.0rc0

14 Nov 19:36
@njzjz njzjz
v3.0.0rc0
0ad4289
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v3.0.0rc0 Pre-release
Pre-release

DeePMD-kit v3: Multiple-backend Framework, DPA-2 Large Atomic Model, and Plugin Mechanisms

We are excited to present the first release candidate of DeePMD-kit v3, an advanced version that enables deep potential models with TensorFlow, PyTorch, or JAX backends. Additionally, DeePMD-kit v3 introduces support for the DPA-2 model, a novel architecture optimized for large atomic models. This release enhances plugin mechanisms, making integrating and developing new models easier.

Highlights

Multiple-backend framework: TensorFlow, PyTorch, and JAX support

image

DeePMD-kit v3 adds a versatile, pluggable framework providing consistent training and inference experience across multiple backends. Version 3.0.0 includes:

  • TensorFlow backend: Known for its computational efficiency with a static graph design.
  • PyTorch backend: A dynamic graph backend that simplifies model extension and development.
  • DP backend: Built with NumPy and Array API, a reference backend for development without heavy deep-learning frameworks.
  • JAX backend: Based on the DP backend via Array API, a static graph backend.
Features TensorFlow PyTorch JAX DP
Descriptor local frame ✅
Descriptor se_e2_a ✅ ✅ ✅ ✅
Descriptor se_e2_r ✅ ✅ ✅ ✅
Descriptor se_e3 ✅ ✅ ✅ ✅
Descriptor se_e3_tebd ✅ ✅ ✅
Descriptor DPA1 ✅ ✅ ✅ ✅
Descriptor DPA2 ✅ ✅ ✅
Descriptor Hybrid ✅ ✅ ✅ ✅
Fitting energy ✅ ✅ ✅ ✅
Fitting dipole ✅ ✅ ✅ ✅
Fitting polar ✅ ✅ ✅ ✅
Fitting DOS ✅ ✅ ✅ ✅
Fitting property ✅ ✅ ✅
ZBL ✅ ✅ ✅ ✅
DPLR ✅
DPRc ✅ ✅ ✅ ✅
Spin ✅ ✅ ✅
Gradient calculation ✅ ✅ ✅
Model training ✅ ✅
Model compression ✅ ✅
Python inference ✅ ✅ ✅ ✅
C++ inference ✅ ✅ ✅

Critical features of the multiple-backend framework include the ability to:

  • Train models using different backends with the same training data and input script, allowing backend switching based on your efficiency or convenience needs.
# Training a model using the TensorFlow backend
dp --tf train input.json
dp --tf freeze
dp --tf compress

# Training a model using the PyTorch backend
dp --pt train input.json
dp --pt freeze
dp --pt compress
  • Convert models between backends using dp convert-backend, with backend-specific file extensions (e.g., .pb for TensorFlow and .pth for PyTorch).
# Convert from a TensorFlow model to a PyTorch model
dp convert-backend frozen_model.pb frozen_model.pth
# Convert from a PyTorch model to a TensorFlow model
dp convert-backend frozen_model.pth frozen_model.pb
# Convert from a PyTorch model to a JAX model
dp convert-backend frozen_model.pth frozen_model.savedmodel
# Convert from a PyTorch model to the backend-independent DP format
dp convert-backend frozen_model.pth frozen_model.dp
  • Run inference across backends via interfaces like dp test, Python/C++/C interfaces, or third-party packages (e.g., dpdata, ASE, LAMMPS, AMBER, Gromacs, i-PI, CP2K, OpenMM, ABACUS, etc.).
# In a LAMMPS file:
# run LAMMPS with a TensorFlow backend model
pair_style deepmd frozen_model.pb
# run LAMMPS with a PyTorch backend model
pair_style deepmd frozen_model.pth
# run LAMMPS with a JAX backend model
pair_style deepmd frozen_model.savedmodel
# Calculate model deviation using different models
pair_style deepmd frozen_model.pb frozen_model.pth frozen_model.savedmodel out_file md.out out_freq 100
  • Add a new backend to DeePMD-kit much more quickly if you want to contribute to DeePMD-kit.

DPA-2 model: Towards a universal large atomic model for molecular and material simulation

The DPA-2 model offers a robust architecture for large atomic models (LAM), accurately representing diverse chemical systems for high-quality simulations. In this release, DPA-2 is trainable in the PyTorch backend, with an example configuration available in examples/water/dpa2. DPA-2 is available for Python inference in the JAX backend.

The DPA-2 descriptor comprises repinit and repformer, as shown below.

DPA-2

The PyTorch backend supports training strategies for large atomic models, including:

  • Parallel training: Train large atomic models on multiple GPUs for efficiency.
torchrun --nproc_per_node=4 --no-python dp --pt train input.json
  • Multi-task training: For large atomic models trained across a broad range of data calculated on different DFT levels with shared descriptors. An example is given in examples/water_multi_task/pytorch_example/input_torch.json.
  • Finetune: Training a pre-train large atomic model on a smaller, task-specific dataset. The PyTorch backend has supported --finetune argument in the dp --pt train command line.

Plugin mechanisms for external models

In v3.0.0, plugin capabilities allow you to develop models with TensorFlow, PyTorch, or JAX, leveraging DeePMD-kit's trainer, loss functions, and interfaces. A plugin example is deepmd-gnn, which supports training the MACE and NequIP models in the DeePMD-kit with the familiar commands.

dp --pt train mace.json
dp --pt freeze
dp --pt test -m frozen_model.pth -s ../data/

image

Other new features

  • Descriptor se_e3_tebd. (#4066)
  • Fitting the property (#3867).
  • New training parameters: max_ckpt_keep (#3441), change_bias_after_training (#3993), and stat_file.
  • New command line interface: dp change-bias (#3993) and dp show (#3796).
  • Support generating JSON schema for integration with VSCode (#3849).
  • The latest LAMMPS version (stable_29Aug2024_update1) is supported. (#4088, #4179)

Breaking changes

  • Python 3.7 and 3.8 supports are dropped. (#3185, #4185)
  • We require all model files to have the correct filename extension for all interfaces so a corresponding backend can load them. TensorFlow model files must end with .pb extension.
  • Bias is removed by default from type embedding. (#3958)
  • The spin model is refactored, and its usage in the LAMMPS module has been changed. (#3301, #4321)
  • Multi-task training support is removed from the TensorFlow backend. (#3763)
  • The set_prefix key is deprecated. (#3753)
  • dp test now uses all sets for training and test. In previous versions, only the last set is used as the test set in dp test. (#3862)
  • The Python module structure is fully refactored. The old deepmd module was moved to deepmd.tf without other API changes, and deepmd_utils was moved to deepmd without other API changes. (#3177, #3178)
  • Python class DeepTensor (including DeepDiople and DeepPolar) now returns atomic tensor in the dimension of natoms instead of nsel_atoms. (#3390)
  • C++ 11 support is dropped. (#4068)

For other changes, refer to Full Changelog: v2.2.11...v3.0.0rc0

Contributors

The PyTorch backend was developed in the dptech-corp/deepmd-pytorch repository, and then it was fully merged into the deepmd-kit repository in #3180. Contributors to the deepmd-pytorch repository:

Contributors to the deepmd-kit repository:

Read more

Contributors

amcadmus, chazeon, and 29 other contributors
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v3.0.0b4

25 Sep 16:01
@njzjz njzjz
v3.0.0b4
0b3f860
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v3.0.0b4 Pre-release
Pre-release

What's Changed

Breaking changes

  • breaking: drop C++ 11 by @njzjz in #4068
  • breaking(pt/dp): tune new sub-structures for DPA2 by @iProzd in #4089
    The default values of new options g1_out_conv and g1_out_mlp are set to True. The behaviors in previous versions are False.

New features

Enhancement

  • fix: bump LAMMPS to stable_29Aug2024 by @njzjz in #4088
  • chore(pt): cleanup deadcode by @wanghan-iapcm in #4142
  • chore(pt): make comm_dict for dpa2 noncompulsory when nghost is 0 by @njzjz in #4144
  • Set ROCM_ROOT to ROCM_PATH when it exist by @sigbjobo in #4150
  • chore(pt): move deepmd.pt.infer.deep_eval.eval_model to tests by @njzjz in #4153

Documentation

  • docs: improve docs for environment variables by @njzjz in #4070
  • docs: dynamically generate command outputs by @njzjz in #4071
  • docs: improve error message for inconsistent type maps by @njzjz in #4074
  • docs: add multiple packages to intersphinx_mapping by @njzjz in #4075
  • docs: document CMake variables using Sphinx styles by @njzjz in #4079
  • docs: update ipi installation command by @njzjz in #4081
  • docs: fix the default value of DP_ENABLE_PYTORCH by @njzjz in #4083
  • docs: fix defination of se_e3 by @njzjz in #4113
  • docs: update DeepModeling URLs by @njzjz-bot in #4119
  • docs(pt): examples for new dpa2 model by @iProzd in #4138

Bugfix

  • fix: fix PT AutoBatchSize OOM bug and merge execute_all into base by @njzjz in #4047
  • fix: replace datetime.datetime.utcnow which is deprecated by @njzjz in #4067
  • fix:fix LAMMPS MPI tests with mpi4py 4.0.0 by @njzjz in #4032
  • fix(pt): invalid type_map when multitask training by @Cloudac7 in #4031
  • fix: manage testing models in a standard way by @njzjz in #4028
  • fix(pt): fix ValueError when array byte order is not native by @njzjz in #4100
  • fix(pt): convert torch.__version__ to str when serializing by @njzjz in #4106
  • fix(tests): fix skip_dp by @njzjz in #4111
  • [Fix] Wrap log_path with Path by @HydrogenSulfate in #4117
  • fix: bugs in uts for property fit by @Chengqian-Zhang in #4120
  • fix: type of the preset out bias by @wanghan-iapcm in #4135
  • fix(pt): fix zero inputs for LayerNorm by @njzjz in #4134
  • fix(pt/dp): share params of repinit_three_body by @iProzd in #4139
  • fix(pt): move entry point from deepmd.pt.model to deepmd.pt by @njzjz in #4146
  • fix: fix DPH5Path.glob for new keys by @njzjz in #4152
  • fix(pt): make state_dict safe for weights_only by @iProzd in #4148
  • fix(pt): fix compute_output_stats_global when atomic_output is None by @njzjz in #4155
  • fix(pt ut): make separated uts deterministic by @iProzd in #4162
  • fix(pt): finetuning property/dipole/polar/dos fitting with multi-dimensional data causes error by @Chengqian-Zhang in #4145

Dependency updates

  • chore(deps): bump scikit-build-core to 0.9.x by @njzjz in #4038
  • build(deps): bump pypa/cibuildwheel from 2.19 to 2.20 by @dependabot in #4045
  • build(deps): bump pypa/cibuildwheel from 2.20 to 2.21 by @dependabot in #4127

CI/CD

  • ci: add include-hidden-files to actions/upload-artifact by @njzjz in #4095
  • ci: test Python 3.12 by @njzjz in #4059
  • CI(codecov): do not notify until all reports are ready by @njzjz in #4136

Full Changelog: v3.0.0b3...v3.0.0b4

Contributors

njzjz, Cloudac7, and 7 other contributors
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v3.0.0b3

27 Jul 04:25
@njzjz njzjz
v3.0.0b3
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Full Changelog: v3.0.0b2...v3.0.0b3

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njzjz
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