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Log and Get Metrics

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

Capturing metrics

You can capture metrics using the log_metrics method.

from truefoundry.ml import get_client

client = get_client()
client.create_ml_repo("iris-demo")
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.

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Filter runs on the basis of metrics

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

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Metrics Overview

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()

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.

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Step-wise metrics

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
)

Updated 25 days ago