> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/automodel/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/automodel/_mcp/server.

# nemo_automodel.components.loggers.loggers

Typed configs for remote loggers (WandB, MLflow, Comet).

Each logger config is a plain dataclass exposing its YAML-configurable fields
plus a `build(...)` method that initialises and returns the logger / run
object.  Loggers are a closed, section-named set (no `_target_` dispatch), so
there is no free builder function — `config.build(...)` is the entry point.

## Module Contents

### Classes

| Name                                                                      | Description                                                          |
| ------------------------------------------------------------------------- | -------------------------------------------------------------------- |
| [`CometConfig`](#nemo_automodel-components-loggers-loggers-CometConfig)   | User-facing Comet ML configuration.                                  |
| [`MLflowConfig`](#nemo_automodel-components-loggers-loggers-MLflowConfig) | User-facing MLflow configuration (maps to the YAML `mlflow:` block). |
| [`WandbConfig`](#nemo_automodel-components-loggers-loggers-WandbConfig)   | User-facing WandB configuration (maps to the YAML `wandb:` block).   |

### Data

[`__all__`](#nemo_automodel-components-loggers-loggers-__all__)

### API

```python
class nemo_automodel.components.loggers.loggers.CometConfig(
    project_name: str = 'automodel',
    workspace: str | None = None,
    api_key: str | None = None,
    experiment_name: str | None = None,
    tags: list[str] = list(),
    auto_metric_logging: bool = False
)
```

Dataclass

User-facing Comet ML configuration.

```python
nemo_automodel.components.loggers.loggers.CometConfig.build(
    model_name: str | None = None
) -> typing.Any
```

Initialise Comet ML and return the logger (active on rank 0).

When `model_name` is provided a `model:<name>` tag is appended and,
if `experiment_name` is empty, one is derived from the model name.

**Parameters:**

Optional model name used to tag the run and derive an
experiment name when none is set.

**Returns:** `Any`

A `CometLogger` instance.

```python
class nemo_automodel.components.loggers.loggers.MLflowConfig(
    experiment_name: str = 'automodel-experiment',
    run_name: str = '',
    tracking_uri: str | None = None,
    artifact_location: str | None = None,
    tags: dict[str, str] = dict(),
    resume: bool = True,
    description: str | None = None,
    flatten_depth: int | None = 1
)
```

Dataclass

User-facing MLflow configuration (maps to the YAML `mlflow:` block).

```python
nemo_automodel.components.loggers.loggers.MLflowConfig.build(
    checkpoint_dir: str | None = None,
    run_config: collections.abc.Mapping[str, typing.Any] | None = None
) -> typing.Any
```

Initialise MLflow on rank 0 and start (or resume) a run.

Installs a `sys.excepthook` so crashed jobs report as FAILED rather
than FINISHED.  On non-rank-0 processes returns `None`.

**Parameters:**

Checkpoint directory used to persist / read the
`mlflow_run_id` sidecar for run resumption.

Full training config dict logged as MLflow params
and as a `config.yaml` artifact.

**Returns:** `Any`

Active `mlflow.entities.Run` on rank 0, or `None`.

```python
class nemo_automodel.components.loggers.loggers.WandbConfig(
    project: str = 'automodel',
    entity: str | None = None,
    name: str = '',
    group: str | None = None,
    tags: list[str] = list(),
    notes: str | None = None,
    extra: dict[str, typing.Any] = dict()
)
```

Dataclass

User-facing WandB configuration (maps to the YAML `wandb:` block).

The named fields are the common ones; any other key under the YAML
`wandb:` block (e.g. `mode`, `dir`, `resume`) is a valid
`wandb.init()` kwarg and is preserved verbatim in `extra` and
forwarded to `wandb.init()`.

```python
nemo_automodel.components.loggers.loggers.WandbConfig.build(
    run_config: collections.abc.Mapping[str, typing.Any] | None = None,
    model_name: str | None = None
) -> typing.Any
```

Initialise WandB and return the run.

**Parameters:**

Full training config dict logged to the WandB run.

Optional model name used to derive the run name
when `name` is empty.

**Returns:** `Any`

Initialised `wandb.Run`.

```python
nemo_automodel.components.loggers.loggers.WandbConfig.from_kwargs(
    kwargs: typing.Any = {}
) -> 'WandbConfig'
```

classmethod

Build from a flat kwargs dict, routing unknown keys to `extra`.

Keys matching a named field are assigned directly; everything else
(valid `wandb.init` params such as `mode`/`dir` that are not
first-class fields here) is preserved in `extra`.

```python
nemo_automodel.components.loggers.loggers.__all__ = ['CometConfig', 'MLflowConfig', 'WandbConfig']
```