Add AzureML in integrations via dedicated callback (#8062)

* first attempt to add AzureML callbacks

* func arg fix

* var name fix, but still won't fix error...

* fixing as in https://discuss.huggingface.co/t/how-to-integrate-an-azuremlcallback-for-logging-in-azure/1713/2



* Avoid lint check of azureml import

* black compliance

* Make isort happy

* Fix point typo in docs

* Add AzureML to Callbacks docs

* Attempt to make sphinx happy

* Format callback docs

* Make documentation style happy

* Make docs compliant to style
Co-authored-by: Davide Fiocco <davide.fiocco@frontiersin.net>

docs/source/main_classes/callback.rst

@@ -13,7 +13,7 @@ subclass :class:`~transformers.Trainer` and override the methods you need (see :
 By default a :class:`~transformers.Trainer` will use the following callbacks:
 
 - :class:`~transformers.DefaultFlowCallback` which handles the default behavior for logging, saving and evaluation.
-- :class:`~transformers.PrinterCallback` or :class:`~transformers.ProrgressCallback` to display progress and print the
+- :class:`~transformers.PrinterCallback` or :class:`~transformers.ProgressCallback` to display progress and print the
   logs (the first one is used if you deactivate tqdm through the :class:`~transformers.TrainingArguments`, otherwise
   it's the second one).
 - :class:`~transformers.integrations.TensorBoardCallback` if tensorboard is accessible (either through PyTorch >= 1.4
@@ -21,6 +21,8 @@ By default a :class:`~transformers.Trainer` will use the following callbacks:
 - :class:`~transformers.integrations.WandbCallback` if `wandb <https://www.wandb.com/>`__ is installed.
 - :class:`~transformers.integrations.CometCallback` if `comet_ml <https://www.comet.ml/site/>`__ is installed.
 - :class:`~transformers.integrations.MLflowCallback` if `mlflow <https://www.mlflow.org/>`__ is installed.
+- :class:`~transformers.integrations.AzureMLCallback` if `azureml-sdk <https://pypi.org/project/azureml-sdk/>`__ is
+  installed.
 
 The main class that implements callbacks is :class:`~transformers.TrainerCallback`. It gets the
 :class:`~transformers.TrainingArguments` used to instantiate the :class:`~transformers.Trainer`, can access that
@@ -50,6 +52,7 @@ Here is the list of the available :class:`~transformers.TrainerCallback` in the
 .. autoclass:: transformers.integrations.MLflowCallback
     :members: setup
 
+.. autoclass:: transformers.integrations.AzureMLCallback
 
 TrainerCallback
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

src/transformers/integrations.py

@@ -61,6 +61,13 @@ except ImportError:
     except ImportError:
         _has_tensorboard = False
 
+try:
+    from azureml.core.run import Run  # noqa: F401
+
+    _has_azureml = True
+except ImportError:
+    _has_azureml = False
+
 try:
     import mlflow  # noqa: F401
 
@@ -68,7 +75,6 @@ try:
 except ImportError:
     _has_mlflow = False
 
-
 # No transformer imports above this point
 
 from .file_utils import is_torch_tpu_available  # noqa: E402
@@ -97,6 +103,10 @@ def is_ray_available():
     return _has_ray
 
 
+def is_azureml_available():
+    return _has_azureml
+
+
 def is_mlflow_available():
     return _has_mlflow
 
@@ -424,6 +434,27 @@ class CometCallback(TrainerCallback):
                 experiment._log_metrics(logs, step=state.global_step, epoch=state.epoch, framework="transformers")
 
 
+class AzureMLCallback(TrainerCallback):
+    """
+    A :class:`~transformers.TrainerCallback` that sends the logs to `AzureML
+    <https://pypi.org/project/azureml-sdk/>`__.
+    """
+
+    def __init__(self, azureml_run=None):
+        assert _has_azureml, "AzureMLCallback requires azureml to be installed. Run `pip install azureml-sdk`."
+        self.azureml_run = azureml_run
+
+    def on_init_end(self, args, state, control, **kwargs):
+        if self.azureml_run is None and state.is_world_process_zero:
+            self.azureml_run = Run.get_context()
+
+    def on_log(self, args, state, control, logs=None, **kwargs):
+        if self.azureml_run:
+            for k, v in logs.items():
+                if isinstance(v, (int, float)):
+                    self.azureml_run.log(k, v, description=k)
+
+
 class MLflowCallback(TrainerCallback):
     """
     A :class:`~transformers.TrainerCallback` that sends the logs to `MLflow <https://www.mlflow.org/>`__.

src/transformers/trainer.py

@@ -31,6 +31,7 @@ from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 from .integrations import (  # isort: split
     default_hp_search_backend,
     hp_params,
+    is_azureml_available,
     is_comet_available,
     is_mlflow_available,
     is_optuna_available,
@@ -154,6 +155,11 @@ if is_optuna_available():
 if is_ray_available():
     from ray import tune
 
+if is_azureml_available():
+    from .integrations import AzureMLCallback
+
+    DEFAULT_CALLBACKS.append(AzureMLCallback)
+
 logger = logging.get_logger(__name__)