mattertune.configs.backbones.jmp
- class mattertune.configs.backbones.jmp.CutoffsConfig(*, main, aeaint, qint, aint)[source]
- Parameters:
main (float)
aeaint (float)
qint (float)
aint (float)
- main: float
- aeaint: float
- qint: float
- aint: float
- class mattertune.configs.backbones.jmp.FinetuneModuleBaseConfig(*, properties, optimizer, lr_scheduler=None, ignore_gpu_batch_transform_error=True, normalizers={})[source]
- Parameters:
properties (Sequence[PropertyConfig])
optimizer (OptimizerConfig)
lr_scheduler (LRSchedulerConfig | None)
ignore_gpu_batch_transform_error (bool)
normalizers (Mapping[str, Sequence[NormalizerConfig]])
- properties: Sequence[PropertyConfig]
Properties to predict.
- optimizer: OptimizerConfig
Optimizer.
- lr_scheduler: LRSchedulerConfig | None
Learning Rate Scheduler
- ignore_gpu_batch_transform_error: bool
Whether to ignore data processing errors during training.
- normalizers: Mapping[str, Sequence[NormalizerConfig]]
Normalizers for the properties.
Any property can be associated with multiple normalizers. This is useful for cases where we want to normalize the same property in different ways. For example, we may want to normalize the energy by subtracting the atomic reference energies, as well as by mean and standard deviation normalization.
The normalizers are applied in the order they are defined in the list.
- abstract classmethod ensure_dependencies()[source]
Ensure that all dependencies are installed.
This method should raise an exception if any dependencies are missing, with a message indicating which dependencies are missing and how to install them.
- class mattertune.configs.backbones.jmp.JMPBackboneConfig(*, properties, optimizer, lr_scheduler=None, ignore_gpu_batch_transform_error=True, normalizers={}, name='jmp', ckpt_path, graph_computer)[source]
- Parameters:
properties (Sequence[PropertyConfig])
optimizer (OptimizerConfig)
lr_scheduler (LRSchedulerConfig | None)
ignore_gpu_batch_transform_error (bool)
normalizers (Mapping[str, Sequence[NormalizerConfig]])
name (Literal['jmp'])
ckpt_path (Path | CachedPath)
graph_computer (JMPGraphComputerConfig)
- name: Literal['jmp']
The type of the backbone.
- ckpt_path: Path | CE.CachedPath
The path to the pre-trained model checkpoint.
- graph_computer: JMPGraphComputerConfig
The configuration for the graph computer.
- classmethod ensure_dependencies()[source]
Ensure that all dependencies are installed.
This method should raise an exception if any dependencies are missing, with a message indicating which dependencies are missing and how to install them.
- properties: Sequence[PropertyConfig]
Properties to predict.
- optimizer: OptimizerConfig
Optimizer.
- lr_scheduler: LRSchedulerConfig | None
Learning Rate Scheduler
- ignore_gpu_batch_transform_error: bool
Whether to ignore data processing errors during training.
- normalizers: Mapping[str, Sequence[NormalizerConfig]]
Normalizers for the properties.
Any property can be associated with multiple normalizers. This is useful for cases where we want to normalize the same property in different ways. For example, we may want to normalize the energy by subtracting the atomic reference energies, as well as by mean and standard deviation normalization.
The normalizers are applied in the order they are defined in the list.
- class mattertune.configs.backbones.jmp.JMPGraphComputerConfig(*, pbc, cutoffs=CutoffsConfig(main=12.0, aeaint=12.0, qint=12.0, aint=12.0), max_neighbors=MaxNeighborsConfig(main=30, aeaint=20, qint=8, aint=1000), per_graph_radius_graph=False)[source]
- Parameters:
pbc (bool)
cutoffs (CutoffsConfig)
max_neighbors (MaxNeighborsConfig)
per_graph_radius_graph (bool)
- pbc: bool
Whether to use periodic boundary conditions.
- cutoffs: CutoffsConfig
The cutoff for the radius graph.
- max_neighbors: MaxNeighborsConfig
The maximum number of neighbors for the radius graph.
- per_graph_radius_graph: bool
Whether to compute the radius graph per graph.
- class mattertune.configs.backbones.jmp.MaxNeighborsConfig(*, main, aeaint, qint, aint)[source]
- Parameters:
main (int)
aeaint (int)
qint (int)
aint (int)
- main: int
- aeaint: int
- qint: int
- aint: int
Modules