add more detailed logging examples (#69)

website/docs/cheatsheets/python/v1/logging.md

@@ -22,8 +22,7 @@ run = Run.get_context()
 run.log('metric-name', metric_value)
 ```
 
-You can log the same metric multiple times within a run, the result being considered a vector
-of that metric.
+You can log the same metric multiple times within a run; the results will be displayed as a chart.
 
 ### `log_row`
 
@@ -35,32 +34,24 @@ run = Run.get_context()
 run.log_row("Y over X", x=1, y=0.4)
 ```
 
-### With MLFlow
-
-Use MLFlowLogger to log metrics.
-
-```python title="script.py"
-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()
-```
-
-#### Example: PyTorch Lightning
-
-```python
-from pytorch_lightning.loggers import MLFlowLogger
-
-mlf_logger = MLFlowLogger(experiment_name=run.experiment.name, tracking_uri=mlflow_url)
-mlf_logger._run_id = run.id
-```
+:::info More logging options
+These are probably the most common APIs used for logging metrics, but see [here](https://docs.microsoft.com/azure/machine-learning/how-to-log-view-metrics#data-types) 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](https://ml.azure.com), or using the SDK:
+
+```
+run.get_workspace_url()
+```
+
+Select the "Metrics" tab and select the metric(s) to view:
+
+![](/img/view-metrics.png)
+
 ### Via the SDK
 
 Viewing metrics in a run (for more details on runs: [Run](run))
@@ -89,4 +80,68 @@ for run in exp.get_runs():
     my_metric = metrics.get('my-metric')
     if my_metric:
         print(my_metric)
+```
+
+## Examples
+
+### Logging with MLFlow
+
+Use [MLFlow](https://mlflow.org/) to log metrics in Azure ML.
+
+```python
+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
+
+```python
+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:
+
+```python
+logger = get_logger()
+
+trainer = pl.Trainer.from_argparse_args(
+    args=args,
+    logger=logger,
+    )
+trainer.fit(model)
 ```