molecular_simulations.simulate.parsl_settings module

class molecular_simulations.simulate.parsl_settings.AuroraSettings(**data)

Bases: BaseComputeSettings

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

• label (str)

• worker_init (str)

• num_nodes (int)

• scheduler_options (str)

• account (str)

• queue (str)

• walltime (str)

• retries (int)

• cpus_per_node (int)

• strategy (str)

• available_accelerators (List[str])

account: str

available_accelerators: List[str]

config_factory(run_dir)

Create a Parsl configuration for running on Aurora.

Return type:

Config

Parameters:

run_dir (str | Path)

cpus_per_node: int

label: str

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

num_nodes: int

queue: str

retries: int

scheduler_options: str

strategy: str

walltime: str

worker_init: str

class molecular_simulations.simulate.parsl_settings.BaseComputeSettings(**data)

Bases: ABC, BaseSettings

Compute settings (HPC platform, number of GPUs, etc).

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

abstractmethod config_factory(run_dir)

Create new Parsl configuration.

Return type:

Config

Parameters:

run_dir (str | Path)

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class molecular_simulations.simulate.parsl_settings.BaseSettings(**data)

Bases: BaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

dump_yaml(filename)

Return type:

None

Parameters:

filename (str | Path)

classmethod from_yaml(filename)

Return type:

TypeVar(_T)

Parameters:

filename (str | Path)

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class molecular_simulations.simulate.parsl_settings.LocalSettings(**data)

Bases: BaseComputeSettings

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

• available_accelerators (int | Sequence[str])

• retries (int)

• label (str)

• worker_port_range (Tuple[int, int])

available_accelerators: Union[int, Sequence[str]]

config_factory()

Create new Parsl configuration.

Return type:

Config

label: str

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

retries: int

worker_port_range: Tuple[int, int]

class molecular_simulations.simulate.parsl_settings.PolarisSettings(**data)

Bases: BaseComputeSettings

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

• label (str)

• num_nodes (int)

• worker_init (str)

• scheduler_options (str)

• account (str)

• queue (str)

• walltime (str)

• cpus_per_node (int)

• strategy (str)

• available_accelerators (int | Sequence[str])

account: str

available_accelerators: Union[int, Sequence[str]]

config_factory(run_dir)

Create a configuration suitable for running all tasks on single nodes of Polaris We will launch 4 workers per node, each pinned to a different GPU :param num_nodes: Number of nodes to use for the MPI parallel tasks :param user_options: Options for which account to use, location of environment files, etc :type run_dir: Union[str, Path] :param run_dir: Directory in which to store Parsl run files. Default: runinfo

Return type:

Config

Parameters:

run_dir (str | Path)

cpus_per_node: int

label: str

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

num_nodes: int

queue: str

scheduler_options: str

strategy: str

walltime: str

worker_init: str