molecular_simulations.utils.parsl_settings module

class molecular_simulations.utils.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

classmethod from_yaml(filename)

Return type:

TypeVar(_T)

model_config: ClassVar[ConfigDict] = {}

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

class molecular_simulations.utils.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

model_config: ClassVar[ConfigDict] = {}

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

class molecular_simulations.utils.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.

available_accelerators: int | Sequence[str]

worker_init: str

nodes: int

retries: int

label: str

worker_port_range: tuple[int, int]

config_factory(run_dir)

Create new Parsl configuration.

Return type:

Config

model_config: ClassVar[ConfigDict] = {}

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

class molecular_simulations.utils.parsl_settings.LocalCPUSettings(**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.

worker_init: str

nodes: int

max_workers_per_node: int

cores_per_worker: float

retries: int

label: str

worker_port_range: tuple[int, int]

available_accelerators: int | Sequence[str]

config_factory(run_dir)

Create new Parsl configuration.

Return type:

Config

model_config: ClassVar[ConfigDict] = {}

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

class molecular_simulations.utils.parsl_settings.HeterogeneousSettings(**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.

worker_init: str

nodes: int

max_workers_per_node: int

cores_per_worker: float

retries: int

worker_port_range: tuple[int, int]

available_accelerators: int

config_factory(run_dir)

Create new Parsl configuration.

Return type:

Config

model_config: ClassVar[ConfigDict] = {}

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

class molecular_simulations.utils.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.

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]

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: str | Path :param run_dir: Directory in which to store Parsl run files. Default: runinfo

Return type:

Config

model_config: ClassVar[ConfigDict] = {}

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

class molecular_simulations.utils.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.

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]

config_factory(run_dir)

Create a Parsl configuration for running on Aurora.

Return type:

Config

model_config: ClassVar[ConfigDict] = {}

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