Metrics are values that help you to evaluate and compare different runs. For example, accuracy, f1 score.

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Capturing metrics

You can capture metrics using the log_metrics method.

from truefoundry.ml import get_client

client = get_client()
run = client.create_run(ml_repo="iris-demo")
run.log_metrics(metric_dict={"accuracy": 0.7, "loss": 0.6})

run.end()

These metrics can be seen in MLFoundry dashboard. Filters can be used on metrics values to filter out runs as shown in the figure.

Filter runs on the basis of metrics

These metrics can also be found in the overview section of run in the dashboard.

Metrics Overview

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Accessing the metrics for a run

You can use the get_metrics method. It returns a dictionary.

from truefoundry.ml import get_client

client = get_client()
run = client.get_run("run-id-of-the-run")

metrics = run.get_metrics()

for metric_name, metric_history in metrics.items():
    print(f"logged metrics for metric {metric_name}:")
    for metric in metric_history:
        print(f"value: {metric.value}")
        print(f"step: {metric.step}")
        print(f"timestamp_ms: {metric.timestamp}")
        print("--")

run.end()

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Step-wise metric logging

You can capture step-wise metrics too using the step argument.

for global_step in range(1000):
    run.log_metrics(metric_dict={"accuracy": 0.7, "loss": 0.6}, step=global_step)

The stepwise-metrics can be visualized as graphs in the dashboard.

Step-wise metrics

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Should I use epoch or global step as a value for the step argument?

If available you should use the global step as a value for the step argument.
To capture epoch-level metric aggregates, you can use the following pattern.

run.log_metrics(
  (metric_dict = { "epoch/train_accuracy": 0.7, epoch: epoch }),
  (step = global_step)
);