> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/gym/llms.txt.
> For full documentation content, see https://docs.nvidia.com/nemo/gym/llms-full.txt.
# nemo_gym.train_data_utils
## Module Contents
### Classes
| Name | Description |
| --------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------- |
| [`Accumulator`](#nemo_gym-train_data_utils-Accumulator) | - |
| [`AvgMinMax`](#nemo_gym-train_data_utils-AvgMinMax) | - |
| [`DatasetMetrics`](#nemo_gym-train_data_utils-DatasetMetrics) | - |
| [`DatasetValidatorState`](#nemo_gym-train_data_utils-DatasetValidatorState) | - |
| [`StringMetrics`](#nemo_gym-train_data_utils-StringMetrics) | - |
| [`TrainDataProcessor`](#nemo_gym-train_data_utils-TrainDataProcessor) | - |
| [`TrainDataProcessorConfig`](#nemo_gym-train_data_utils-TrainDataProcessorConfig) | Prepare and validate training data, generating metrics and statistics for datasets. |
### Functions
| Name | Description |
| ----------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------- |
| [`aggregate_other_metrics`](#nemo_gym-train_data_utils-aggregate_other_metrics) | Combines misc items (those other than response/response create params) into current metrics |
| [`compute_sample_metrics`](#nemo_gym-train_data_utils-compute_sample_metrics) | - |
| [`postprocess_other_metrics`](#nemo_gym-train_data_utils-postprocess_other_metrics) | Aggregates metrics and merges current metrics (containing only AvgMinMax) with StringMetrics |
| [`prepare_data`](#nemo_gym-train_data_utils-prepare_data) | - |
| [`validate_backend_credentials`](#nemo_gym-train_data_utils-validate_backend_credentials) | Check if required env variables are present for the chosen backend |
### API
```python
class nemo_gym.train_data_utils.Accumulator()
```
**Bases:** `BaseModel`
```python
nemo_gym.train_data_utils.Accumulator._add(
other: typing.Self
) -> None
```
abstract
```python
nemo_gym.train_data_utils.Accumulator._aggregate() -> typing.Self
```
abstract
```python
nemo_gym.train_data_utils.Accumulator.add(
other: typing.Self
) -> None
```
```python
nemo_gym.train_data_utils.Accumulator.aggregate() -> typing.Self
```
```python
class nemo_gym.train_data_utils.AvgMinMax()
```
**Bases:** [Accumulator](#nemo_gym-train_data_utils-Accumulator)
```python
nemo_gym.train_data_utils.AvgMinMax._add(
other: typing.Self
) -> None
```
```python
nemo_gym.train_data_utils.AvgMinMax._aggregate() -> typing.Self
```
```python
nemo_gym.train_data_utils.AvgMinMax.observe(
x: float
) -> None
```
```python
class nemo_gym.train_data_utils.DatasetMetrics()
```
**Bases:** [Accumulator](#nemo_gym-train_data_utils-Accumulator)
```python
nemo_gym.train_data_utils.DatasetMetrics._add(
other: typing.Self
) -> None
```
```python
nemo_gym.train_data_utils.DatasetMetrics._aggregate() -> typing.Self
```
```python
class nemo_gym.train_data_utils.DatasetValidatorState()
```
**Bases:** `BaseModel`
```python
class nemo_gym.train_data_utils.StringMetrics()
```
**Bases:** `BaseModel`
```python
class nemo_gym.train_data_utils.TrainDataProcessor()
```
**Bases:** `BaseModel`
```python
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]
) -> typing.List[pathlib.Path]
```
```python
nemo_gym.train_data_utils.TrainDataProcessor._iter_dataset_lines(
dataset_config: nemo_gym.config_types.DatasetConfig
)
```
```python
nemo_gym.train_data_utils.TrainDataProcessor._print_title(
title: str
) -> None
```
```python
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
```python
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
```
```python
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
```
```python
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
```
```python
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]
```
```python
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
```
```python
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"
```python
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]
```
```python
class nemo_gym.train_data_utils.TrainDataProcessorConfig()
```
**Bases:** [BaseNeMoGymCLIConfig](/nemo-gym/nemo_gym/config_types#nemo_gym-config_types-BaseNeMoGymCLIConfig)
Prepare and validate training data, generating metrics and statistics for datasets.
Examples:
```python
config_paths="resources_servers/example_multi_step/configs/example_multi_step.yaml,\
responses_api_models/openai_model/configs/openai_model.yaml"
ng_prepare_data "+config_paths=[${config_paths}]" +output_dirpath=data/example_multi_step +mode=example_validation
```
```python
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
```python
nemo_gym.train_data_utils.compute_sample_metrics(
sample_dict_str: str
) -> typing.Tuple[nemo_gym.train_data_utils.DatasetMetrics, bool]
```
```python
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
```python
nemo_gym.train_data_utils.prepare_data()
```
```python
nemo_gym.train_data_utils.validate_backend_credentials(
backend: str
) -> tuple[bool, str]
```
Check if required env variables are present for the chosen backend