Log and Get Metrics
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()
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?
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 2 months ago