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# nemo_automodel.components.loggers

## Submodules

* **[`nemo_automodel.components.loggers.comet_utils`](/nemo-automodel/nemo_automodel/components/loggers/comet_utils)**
* **[`nemo_automodel.components.loggers.log_utils`](/nemo-automodel/nemo_automodel/components/loggers/log_utils)**
* **[`nemo_automodel.components.loggers.loggers`](/nemo-automodel/nemo_automodel/components/loggers/loggers)**
* **[`nemo_automodel.components.loggers.metric_logger`](/nemo-automodel/nemo_automodel/components/loggers/metric_logger)**
* **[`nemo_automodel.components.loggers.mlflow_utils`](/nemo-automodel/nemo_automodel/components/loggers/mlflow_utils)**
* **[`nemo_automodel.components.loggers.wandb_utils`](/nemo-automodel/nemo_automodel/components/loggers/wandb_utils)**

## Package 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).   |

### 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.

**`api_key`** `str | None = None`

Comet API key.  `None` reads from `COMET_API_KEY` env var.

---

**`auto_metric_logging`** `bool = False`

Enable Comet's automatic metric logging.

---

**`experiment_name`** `str | None = None`

Display name for this experiment run.

---

**`project_name`** `str = 'automodel'`

Comet project name.

---

**`tags`** `list[str] = field(default_factory=list)`

List of string tags attached to the experiment.

---

**`workspace`** `str | None = None`

Comet workspace.  `None` uses the default.

---

```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:**

**`model_name`** `str | None` — default: None

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).

**`artifact_location`** `str | None = None`

Root artifact store URI for the experiment.

---

**`description`** `str | None = None`

Free-text description shown in the MLflow UI
(sets the `mlflow.note.content` tag).

---

**`experiment_name`** `str = 'automodel-experiment'`

MLflow experiment name.

---

**`flatten_depth`** `int | None = 1`

Nesting depth for `mlflow.log_params`.
`1` (default) splits one level; `None` is fully recursive.

---

**`resume`** `bool = True`

When `True` (default), look for a `mlflow_run_id`
sidecar in the checkpoint dir and resume that run.

---

**`run_name`** `str = ''`

Display name for the run.

---

**`tags`** `dict[str, str] = field(default_factory=dict)`

Dictionary of string tags attached to the run.

---

**`tracking_uri`** `str | None = None`

MLflow tracking server URI. `None` delegates to
`MLFLOW_TRACKING_URI` and then MLflow's local default
(`sqlite:///mlflow.db` in current releases).

---

```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_dir`** `str | None` — default: None

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

---

**`run_config`** `Mapping[str, Any] | None` — default: None

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,
    swanlab: nemo_automodel.components.loggers.loggers.SwanLabConfig | 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()`.

**`entity`** `str | None = None`

WandB team / entity.  `None` uses the default from
the wandb config or `WANDB_ENTITY` env var.

---

**`extra`** `dict[str, Any] = field(default_factory=dict)`

Any additional `wandb.init()` kwargs (`mode`, `dir`,
...) carried through unfiltered.

---

**`group`** `str | None = None`

Group name for related runs.

---

**`name`** `str = ''`

Display name for the run.  When empty, `build` derives
one from the model name.

---

**`notes`** `str | None = None`

Free-text notes shown in the WandB UI.

---

**`project`** `str = 'automodel'`

WandB project name.

---

**`swanlab`** `SwanLabConfig | None = None`

Optional `SwanLabConfig` that mirrors this run to
SwanLab (the `wandb.swanlab:` sub-block).

---

**`tags`** `list[str] = field(default_factory=list)`

List of string tags attached to the run.

---

```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:**

**`run_config`** `Mapping[str, Any] | None` — default: None

Full training config dict logged to the WandB run.

---

**`model_name`** `str | None` — default: None

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`.