Report Metrics¶

The Core API makes it easy to report training and validation metrics to the master during training with only a few new lines of code.

For this example, create a new training script called 1_metrics.py by copying the 0_start.py script from Getting Started.
Begin by importing import the determined module:
```
import determined as det
```

Enable logging, using the det.LOG_FORMAT for logs. This enables useful log messages from the determined library, and det.LOG_FORMAT enables filter-by-level in the WebUI.

    logging.basicConfig(level=logging.DEBUG, format=det.LOG_FORMAT)
    # Log at different levels to demonstrate filter-by-level in the WebUI.
    logging.debug("debug-level message")
    logging.info("info-level message")
    logging.warning("warning-level message")
    logging.error("error-level message")

In your if __name__ == "__main__" block, wrap the entire execution of main() within the scope of determined.core.init(), which prepares resources for training and cleans them up afterward. Add the core_context as a new argument to main() because the Core API is accessed through the core_context object.
```
    with det.core.init() as core_context:
        main(core_context=core_context, increment_by=1)
```

Within main(), add two calls: (1) report training metrics periodically during training and (2) report validation metrics every time a validation runs.

def main(core_context, increment_by):
    x = 0
    for batch in range(100):
        x += increment_by
        steps_completed = batch + 1
        time.sleep(0.1)
        logging.info(f"x is now {x}")
        # NEW: report training metrics.
        if steps_completed % 10 == 0:
            core_context.train.report_training_metrics(
                steps_completed=steps_completed, metrics={"x": x}
            )
    # NEW: report a "validation" metric at the end.
    core_context.train.report_validation_metrics(steps_completed=steps_completed, metrics={"x": x})

The report_validation_metrics() call typically happens after the validation step, however, actual validation is not demonstrated by this example.

Create a 1_metrics.yaml file with an entrypoint invoking the new 1_metrics.py file. You can copy the 0_start.yaml configuration file and change the first couple of lines:
```
name: core-api-stage-1
entrypoint: python3 1_metrics.py
```
Run the code using the command:
```
det e create 1_metrics.yaml . -f
```
You can now navigate to the new experiment in the WebUI and view the plot populated with the training and validation metrics.

The complete 1_metrics.py and 1_metrics.yaml listings used in this example can be found in the core_api.tgz download or in the Github repository.