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Metrics

Logging metrics#

Logging a metric to a run causes that metric to be stored in the run record in the experiment. Visualize and keep a history of all logged metrics.

log#

Log a single metric value to a run.

from azureml.core import Run
run = Run.get_context()
run.log('metric-name', metric_value)

You can log the same metric multiple times within a run; the results will be displayed as a chart.

log_row#

Log a metric with multiple columns.

from azureml.core import Run
run = Run.get_context()
run.log_row("Y over X", x=1, y=0.4)
More logging options

These are probably the most common APIs used for logging metrics, but see here for a complete list, including logging lists, tables and images.

Viewing metrics#

Metrics will be automatically available in the Azure ML Studio. Locate your run, e.g., either by visiting ml.azure.com, or using the SDK:

run.get_workspace_url()

Select the "Metrics" tab and select the metric(s) to view:

Via the SDK#

Viewing metrics in a run (for more details on runs: Run)

metrics = run.get_metrics()
# metrics is of type Dict[str, List[float]] mapping mertic names
# to a list of the values for that metric in the given run.
metrics.get('metric-name')
# list of metrics in the order they were recorded

To view all recorded values for a given metric my-metric in a given experiment my-experiment:

experiments = ws.experiments
# of type Dict[str, Experiment] mapping experiment names the
# corresponding Experiment
exp = experiments['my-experiment']
for run in exp.get_runs():
metrics = run.get_metrics()
my_metric = metrics.get('my-metric')
if my_metric:
print(my_metric)

Examples#

Logging with MLFlow#

Use MLFlow to log metrics in Azure ML.

from azureml.core import Run
# connect to the workspace from within your running code
run = Run.get_context()
ws = run.experiment.workspace
# workspace has associated ml-flow-tracking-uri
mlflow_url = ws.get_mlflow_tracking_uri()

Logging with PyTorch Lightning#

This examples:

  • Includes Lightning's TensorBoardLogger
  • Sets up Lightning's MLFlowLogger using AzureML Run.get_context()
    • Only adds this logger when used as part of an Azure ML run
import pytorch_lightning as pl
run = None
try:
from azureml.core.run import Run, _OfflineRun
run = Run.get_context()
if isinstance(run, _OfflineRun):
run = None
except ImportError:
print("Couldn't import azureml.core.run.Run")
def get_logger():
tb_logger = pl.loggers.TensorBoardLogger('logs/')
logger = [tb_logger]
if run is not None:
mlflow_url = run.experiment.workspace.get_mlflow_tracking_uri()
mlf_logger = pl.loggers.MLFlowLogger(
experiment_name=run.experiment.name,
tracking_uri=mlflow_url,
)
mlf_logger._run_id = run.id
logger.append(mlf_logger)
return logger

Now include this logger in the lightning Trainer class:

logger = get_logger()
trainer = pl.Trainer.from_argparse_args(
args=args,
logger=logger,
)
trainer.fit(model)
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