> ## Documentation Index
> Fetch the complete documentation index at: https://training-docs.cerebras.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Logging

The [`Trainer`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/index.html) class features an extendable logging mechanism that can be used to log metrics to various backends.

On this page, you will learn about how to set up logging to the console via the [`Logging`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/generated/cerebras.modelzoo.trainer.callbacks.html#cerebras.modelzoo.trainer.callbacks.Logging) class as well as how to add [`Logger`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/generated/cerebras.modelzoo.trainer.loggers.html#cerebras.modelzoo.trainer.loggers.Logger) classes to the [`Trainer`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/index.html) as well.

## Prerequisites

Please ensure that you have read through the Cerebras Model Zoo Trainer Overview beforehand. The rest of this page assumes that you already have at least a cursory understanding of what the Cerebras Model Zoo Trainer is and how to use the python API.

Also, make sure that you've read through [Customizing the Trainer with Callbacks](../../../model-zoo/components/trainer-components/customizing-the-trainer-with-callbacks) as this page will assume that you are familiar with the [`Callback`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/generated/cerebras.modelzoo.trainer.callbacks.html#cerebras.modelzoo.trainer.callbacks.Callback) mechanism.

## Logging to Console

The [`Trainer`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/index.html) exposes a [`logger`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/generated/cerebras.modelzoo.trainer.loggers.html#cerebras.modelzoo.trainer.loggers.Logger) attr which returns a Python logger object which can be used to log various messages to the console with different levels.

For example,

```python theme={null}
from cerebras.modelzoo import Trainer

trainer = Trainer(...)

trainer.loggers.info("This is an INFO message")
trainer.loggers.debug("This is a DEBUG message")
trainer.loggers.warning("This is a WARNING message")
trainer.loggers.error("This is a ERROR message")

```

The [`logger`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/generated/cerebras.modelzoo.trainer.loggers.html#cerebras.modelzoo.trainer.loggers.Logger) can be configured by passing in a [`Logging`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/generated/cerebras.modelzoo.trainer.callbacks.html#cerebras.modelzoo.trainer.callbacks.Logging) object to the `Trainer`’s constructor.

<CodeGroup>
  ```python Python theme={null}

  from cerebras.modelzoo import Trainer
  from cerebras.modelzoo.trainer.callbacks import Logging

  trainer = Trainer(
      ...,
      logging=Logging(
          log_steps=1,
          log_level="INFO",
      ),
      ...,
  )
  ...

  ```

  ```yaml YAML theme={null}
  trainer:
    init:
    ...
    logging:
    log_steps:  1
    log_level:  INFO
    ...
    ...

  ```
</CodeGroup>

In the above example, the [`logger`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/generated/cerebras.modelzoo.trainer.loggers.html#cerebras.modelzoo.trainer.loggers.Logger) has been configured to print `INFO` messages to the console by default.

See [Control Logging Frequency](#control-logging-frequency) for an explanation of the `log_steps` argument.

## Logging Metrics

The way to log metrics using the [`Trainer`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/index.html) is to construct and pass in [`Logger`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/generated/cerebras.modelzoo.trainer.loggers.html#cerebras.modelzoo.trainer.loggers.Logger) subclasses.

Included out-of-the-box are

* [`ProgressLogger`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/generated/cerebras.modelzoo.trainer.loggers.html#cerebras.modelzoo.trainer.loggers.ProgressLogger): Logs progress metrics to the console

* [`TensorBoardLogger`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/generated/cerebras.modelzoo.trainer.loggers.html#cerebras.modelzoo.trainer.loggers.TensorBoardLogger): Logs metrics to a TensorBoard event file.

These [`Logger`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/generated/cerebras.modelzoo.trainer.loggers.html#cerebras.modelzoo.trainer.loggers.Logger) subclasses can be constructed and passed into the trainer via the `loggers` argument:

<CodeGroup>
  ```python Python theme={null}

  from cerebras.modelzoo import Trainer
  from cerebras.modelzoo.trainer.loggers import (
        ProgressLogger,
        TensorBoardLogger,
  )

  trainer = Trainer(
      ...,
      loggers=[
          ProgressLogger(),
          TensorBoardLogger(),
      ]
      ...,
  )
  ...

  ```

  ```yaml YAML theme={null}

  trainer:
    init:
    ...
    loggers:
    -  ProgressLogger:  {}
    -  TensorBoardLogger:  {}
    ...
    ...

  ```
</CodeGroup>

With these loggers, you can now call [`trainer.log_metrics`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/index.html#cerebras.modelzoo.Trainer.log_metrics) to log some metric to all loggers.

```python theme={null}
from cerebras.modelzoo import Trainer
from cerebras.modelzoo.trainer.loggers import TensorBoardLogger

trainer = Trainer(
    ...,
    loggers=[TensorBoardLogger()]
    ...,
)

trainer.log_metrics(loss=...)
```

In the above example, the `loss` is being logged to the [`TensorBoardLogger`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/generated/cerebras.modelzoo.trainer.loggers.html#cerebras.modelzoo.trainer.loggers.TensorBoardLogger) at the current global step.

## Logging Name Scope

The trainer also features a [`name_scope`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/index.html#cerebras.modelzoo.Trainer.name_scope) mechanism for logging which is intended to be used to group related logs together.

```python theme={null}
from cerebras.modelzoo import Trainer
from cerebras.modelzoo.trainer.loggers import TensorBoardLogger

trainer = Trainer(
    ...,
    loggers=[TensorBoardLogger()]
    ...,
)

with trainer.name_scope("train"):
    trainer.log_metrics(loss=...)
    trainer.log_metrics(accuracy=...)

```

In the above example, the metrics get recorded in the log as `train/loss` and `train/accuracy`.

## Control Logging Frequency

It is often the case in very long runs that logging metrics every step is undesirably verbose. To remedy this, you can specify `log_steps` to the [`Logging`](ttps://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/generated/cerebras.modelzoo.trainer.callbacks.html#cerebras.modelzoo.trainer.callbacks.Logging) class.

<CodeGroup>
  ```python Python theme={null}

  from cerebras.modelzoo import Trainer
  from cerebras.modelzoo.trainer.callbacks import Logging

  trainer = Trainer(
      ...,
      logging=Logging(log_steps=10),
      ...,
  )
  ...
  ```

  ```yaml YAML theme={null}

  trainer:
    init:
    ...
    logging:
    log_steps:  10
    ...
    ...

  ```
</CodeGroup>

In the above example, the trainer is configured to log metrics every 10 steps. This means that even if [`log_metrics`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/index.html#cerebras.modelzoo.Trainer.log_metrics) is called every step, only every 10 steps does the metric actually get logged.

To query whether or not current step is a log step, you can call [`trainer.is_log_step`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/index.html#cerebras.modelzoo.Trainer.is_log_step).

## Writing a Custom Logger

Now that you know all about the [`Logger`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/generated/cerebras.modelzoo.trainer.loggers.html#cerebras.modelzoo.trainer.loggers.Logger") class and how it’s integrated into the [`Trainer`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/index.html) class, it is fairly straightforward to write your own custom loggers.

To write your own custom Logger class, all you need to do is inherit from the base [`Logger`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/generated/cerebras.modelzoo.trainer.loggers.html#cerebras.modelzoo.trainer.loggers.Logger") class and override the following methods:

* `log_metrics`: Logs the provided metrics at the provided step.

* `flush`: Flushes the logs

For example, let’s implement a simple logger that just logs the metrics to console

```python theme={null}
from cerebras.modelzoo.trainer.logger import Logger

class ConsoleLogger(Logger):
    def setup(self, trainer):
        self.trainer = trainer

    def flush(self):
        for handler in self.trainer.loggers.handlers:
            handler.flush()

    def log_metrics(self, metrics, step):
        for name, value in metrics.items():
            self.trainer.loggers.info(
                f"Step={step}, {name}={value}
            )
```

<Note>
  Note

  All [`Logger`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/generated/cerebras.modelzoo.trainer.loggers.html#cerebras.modelzoo.trainer.loggers.Logger") instances inherit from [`Callback`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/generated/cerebras.modelzoo.trainer.callbacks.html#cerebras.modelzoo.trainer.callbacks.Callback). This means that loggers may override any of the hooks that are exposed via the [`Callback`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/generated/cerebras.modelzoo.trainer.callbacks.html#cerebras.modelzoo.trainer.callbacks.Callback) mechanism too.
</Note>

That is all there is to it. This logger can now be used inside the [`Trainer`](https://training-api.cerebras.ai/en/latest/wsc/Model-zoo/api/index.html) as follows:

<CodeGroup>
  ```python Python theme={null}

  from cerebras.modelzoo import Trainer

  trainer = Trainer(
      ...,
      logger=[ConsoleLogger()],
      ...
  )
  ...

  ```

  ```yaml YAML theme={null}

  As long as the logger class exists in the Python global namespace, you can add any custom logger to a YAML configuration file in exactly the same way you would any other pre-packaged loggers.

  trainer:
    init:
    ...
    loggers:
    -  ConsoleLogger:  {}
    ...

  ```
</CodeGroup>

In order for the callback class to exist in the Python global namespace, the Python interpreter must have seen it at some point. Implementing your custom logger in the same file as the model class or ensuring it is properly imported and referenced in your CLI workflow are two ways to ensure that the logger is recognized and loaded into the Python global namespace.