nemo_gym.train_data_utils

Module Contents

Classes

Name	Description
`Accumulator`	-
`AvgMinMax`	-
`DatasetMetrics`	-
`DatasetValidatorState`	-
`StringMetrics`	-
`TrainDataProcessor`	-
`TrainDataProcessorConfig`	Prepare and validate training data, generating metrics and statistics for datasets.

Functions

Name	Description
`aggregate_other_metrics`	Combines misc items (those other than response/response create params) into current metrics
`compute_sample_metrics`	-
`postprocess_other_metrics`	Aggregates metrics and merges current metrics (containing only AvgMinMax) with StringMetrics
`validate_backend_credentials`	Check if required env variables are present for the chosen backend

Data

__getattr__

API

class nemo_gym.train_data_utils.Accumulator()

Bases: BaseModel

is_aggregated

bool = Field(default=False, exclude=True)

nemo_gym.train_data_utils.Accumulator._add(
    other: typing.Self
) -> None

abstract

nemo_gym.train_data_utils.Accumulator._aggregate() -> typing.Self

abstract

nemo_gym.train_data_utils.Accumulator.add(
    other: typing.Self
) -> None

nemo_gym.train_data_utils.Accumulator.aggregate() -> typing.Self

class nemo_gym.train_data_utils.AvgMinMax()

Bases: Accumulator

float = Field(default=0, exclude=True)

average

float = Field(serialization_alias='Average', default=0)

max

float

mean

float = Field(default=0, exclude=True)

min

float

model_config

= ConfigDict(arbitrary_types_allowed=True)

stddev

float

total

int

nemo_gym.train_data_utils.AvgMinMax._add(
    other: typing.Self
) -> None

nemo_gym.train_data_utils.AvgMinMax._aggregate() -> typing.Self

nemo_gym.train_data_utils.AvgMinMax.observe(
    x: float
) -> None

class nemo_gym.train_data_utils.DatasetMetrics()

Bases: Accumulator

json_dumped_number_of_words

AvgMinMax

model_config

= ConfigDict(extra='allow')

number_of_examples

int

number_of_tools

AvgMinMax

number_of_turns

AvgMinMax

temperature

AvgMinMax

nemo_gym.train_data_utils.DatasetMetrics._add(
    other: typing.Self
) -> None

nemo_gym.train_data_utils.DatasetMetrics._aggregate() -> typing.Self

class nemo_gym.train_data_utils.DatasetValidatorState()

Bases: BaseModel

key_counts

Counter = Field(default_factory=Counter)

metrics

DatasetMetrics = Field(default_factory=DatasetMetrics)

model_config

= ConfigDict(arbitrary_types_allowed=True)

offending_example_idxs

List[int] = Field(default_factory=list)

other_metrics

Dict[str, Any] = Field(default_factory=dict)

class nemo_gym.train_data_utils.StringMetrics()

Bases: BaseModel

total_count

int

unique_count

int

class nemo_gym.train_data_utils.TrainDataProcessor()

Bases: BaseModel

nemo_gym.train_data_utils.TrainDataProcessor._collate_samples_single_type(
    type: nemo_gym.config_types.DatasetType,
    server_instance_configs: typing.List[nemo_gym.config_types.ServerInstanceConfig],
    task_data_validation: str = 'warn'
) -> typing.List[pathlib.Path]

nemo_gym.train_data_utils.TrainDataProcessor._iter_dataset_lines(
    dataset_config: nemo_gym.config_types.DatasetConfig
)

nemo_gym.train_data_utils.TrainDataProcessor._owning_resources_server_impl(
    c: nemo_gym.config_types.ServerInstanceConfig,
    server_instance_configs: typing.List[nemo_gym.config_types.ServerInstanceConfig]
) -> typing.Optional[str]

staticmethod

The resources-server implementation (directory) that owns a declaring instance’s data.

Datasets declared by a resources server belong to its own implementation; datasets declared by an agent belong to the resources server the agent references. Self-contained agents (no resources_server reference) return None here; the validator lookup then falls back to the agent’s own directory.

nemo_gym.train_data_utils.TrainDataProcessor._print_title(
    title: str
) -> None

nemo_gym.train_data_utils.TrainDataProcessor._task_data_validator_for(
    c: nemo_gym.config_types.ServerInstanceConfig,
    d: typing.Union[nemo_gym.config_types.DatasetConfig, nemo_gym.config_types.BenchmarkDatasetConfig],
    server_instance_configs: typing.List[nemo_gym.config_types.ServerInstanceConfig]
) -> typing.Optional[nemo_gym.task_data.TaskDataValidator]

classmethod

nemo_gym.train_data_utils.TrainDataProcessor._validate_aggregate_metrics(
    aggregate_metrics_dict: typing.Dict,
    metrics_fpath: pathlib.Path
) -> typing.Optional[pathlib.Path]

Returns the conflicting metrics fpath if invalid. Else returns None

nemo_gym.train_data_utils.TrainDataProcessor._validate_samples_and_aggregate_metrics_single_dataset(
    dataset_config: nemo_gym.config_types.DatasetConfig
) -> nemo_gym.train_data_utils.DatasetValidatorState

nemo_gym.train_data_utils.TrainDataProcessor._validate_samples_and_aggregate_metrics_single_sample(
    state: nemo_gym.train_data_utils.DatasetValidatorState,
    sample_idx: int,
    sample_dict_str: str
) -> None

nemo_gym.train_data_utils.TrainDataProcessor.collate_samples(
    config: nemo_gym.train_data_utils.TrainDataProcessorConfig,
    server_instance_configs: typing.List[nemo_gym.config_types.ServerInstanceConfig],
    dataset_type_to_aggregate_metrics: typing.Dict[str, nemo_gym.train_data_utils.DatasetMetrics]
) -> None

nemo_gym.train_data_utils.TrainDataProcessor.load_and_validate_server_instance_configs(
    config: nemo_gym.train_data_utils.TrainDataProcessorConfig,
    global_config_dict: omegaconf.DictConfig
) -> typing.List[nemo_gym.config_types.ServerInstanceConfig]

nemo_gym.train_data_utils.TrainDataProcessor.load_datasets(
    config: nemo_gym.train_data_utils.TrainDataProcessorConfig,
    server_instance_configs: typing.List[nemo_gym.config_types.ServerInstanceConfig]
) -> None

nemo_gym.train_data_utils.TrainDataProcessor.run(
    global_config_dict: omegaconf.DictConfig
)

See the README section “How To: Prepare and validate data for PR submission or RL training”

nemo_gym.train_data_utils.TrainDataProcessor.validate_samples_and_aggregate_metrics(
    server_instance_configs: typing.List[nemo_gym.config_types.ServerInstanceConfig],
    overwrite_metrics_conflicts: bool
) -> typing.Dict[str, nemo_gym.train_data_utils.DatasetMetrics]

class nemo_gym.train_data_utils.TrainDataProcessorConfig()

Bases: BaseNeMoGymCLIConfig

Prepare and validate training data, generating metrics and statistics for datasets.

Examples:

config_paths="resources_servers/example_multi_step/configs/example_multi_step.yaml,\
responses_api_models/openai_model/configs/openai_model.yaml"
gym dataset collate "+config_paths=[${config_paths}]"         +output_dirpath=data/example_multi_step         +mode=example_validation

data_source

Literal['gitlab', 'huggingface']

effective_task_data_validation

str

in_scope_dataset_types

List[DatasetType]

mode

Union[Literal['train_preparation'], Literal['example_validation']]

output_dirpath

str

overwrite_metrics_conflicts

bool

should_download

bool

task_data_validation

Literal['off', 'warn', 'error', 'auto']

nemo_gym.train_data_utils.aggregate_other_metrics(
    metrics: typing.Dict[str, typing.Any],
    sample: typing.Dict[str, typing.Any]
) -> None

Combines misc items (those other than response/response create params) into current metrics

nemo_gym.train_data_utils.compute_sample_metrics(
    sample_dict_str: str
) -> typing.Tuple[nemo_gym.train_data_utils.DatasetMetrics, bool]

nemo_gym.train_data_utils.postprocess_other_metrics(
    metrics: nemo_gym.train_data_utils.DatasetMetrics,
    other_metrics: typing.Dict[str, typing.Any]
) -> None

Aggregates metrics and merges current metrics (containing only AvgMinMax) with StringMetrics

nemo_gym.train_data_utils.validate_backend_credentials(
    backend: str
) -> tuple[bool, str]

Check if required env variables are present for the chosen backend

nemo_gym.train_data_utils.__getattr__ = moved_attr_getter(__name__, {'prepare_data': 'nemo_gym.cli.dataset'})