Metrics

note

このコンテンツはお使いの言語では利用できません。

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)