class_docstr: Log losses, metrics, model architecture summary to AzureML.
  
  If `log_offline` is False, will only log if actually running on AzureML.
  A custom AzureML `Run` class can be passed as `azurerun`.
  If `log_to_parent` is True, will also log to the parent run, if exists (e.g. in AzureML pipelines).
class_docstr_tok: ["Log","losses",",","metrics",",","model","architecture","summary","to","AzureML",".","If","`","log_offline","`","is","False",",","will","only","log","if","actually","running","on","AzureML",".","A","custom","AzureML","`","Run","`","class","can","be","passed","as","`","azurerun","`",".","If","`","log_to_parent","`","is","True",",","will","also","log","to","the","parent","run",",","if","exists","(","e.g",".","in","AzureML","pipelines",")","."]
code: def __init__(self, azurerun=None, log_to_parent=True):
      if azurerun:
          self.azurerun = azurerun
      else:
          try:
              self.azurerun = Run.get_context(allow_offline=False)
          except RunEnvironmentException:
              # running locally
              self.azurerun = None
              warnings.warn(
                  "Not running on AzureML and no azurerun passed, AzureMLCallback will be disabled."
              )
      self.log_to_parent = log_to_parent
  
file_path: fastai/fastai/callback/azureml.py
imports: from ..basics import *
  from ..learner import Callback
  from azureml.core.run import Run
  from azureml.exceptions import RunEnvironmentException
  import warnings
label_desc: Use image node_id 1 to create a new AzureMLCallback object from inherited base classes: Callback with example: obj = AzureMLCallback(azurerun, log_to_parent)