include in loggers

src/sparseml/pytorch/utils/logger.py

@@ -45,11 +45,21 @@
     wandb = None
     wandb_err = err
 
+
+try:
+    from clearml import Task
+
+    clearml_err = None
+except Exception as err:
+    clearml = None
+    clearml_err = err
+
 from sparseml.utils import ALL_TOKEN, create_dirs
 
 
 __all__ = [
     "BaseLogger",
+    "ClearMLLogger",
     "LambdaLogger",
     "PythonLogger",
     "TensorBoardLogger",
@@ -628,6 +638,98 @@ def save(
         return True
 
 
+class ClearMLLogger(LambdaLogger):
+    @staticmethod
+    def available() -> bool:
+        """
+        :return: True if wandb is available and installed, False, otherwise
+        """
+        return not clearml_err
+
+    def __init__(
+        self,
+        name: str = "clearml",
+        enabled: bool = True,
+        project_name: str = "sparseml",
+        task_name: str = "",
+    ):
+        if task_name == "":
+            now = datetime.now()
+            task_name = now.strftime("%d-%m-%Y_%H.%M.%S")
+
+        self.task = Task.init(project_name=project_name, task_name=task_name)
+
+        super().__init__(
+            lambda_func=self.log_scalar,
+            name=name,
+            enabled=enabled,
+        )
+
+    def log_hyperparams(
+        self,
+        params: Dict,
+        level: Optional[int] = None,
+    ) -> bool:
+        """
+        :param params: Each key-value pair in the dictionary is the name of the
+            hyper parameter and it's corresponding value.
+        :return: True if logged, False otherwise.
+        """
+        if not self.enabled:
+            return False
+
+        self.task.connect(params)
+        return True
+
+    def log_scalar(
+        self,
+        tag: str,
+        value: float,
+        step: Optional[int] = None,
+        wall_time: Optional[float] = None,
+        level: Optional[int] = None,
+    ) -> bool:
+        """
+        :param tag: identifying tag to log the value with
+        :param value: value to save
+        :param step: global step for when the value was taken
+        :param wall_time: global wall time for when the value was taken,
+            defaults to time.time()
+        :param kwargs: additional logging arguments to support Python and custom loggers
+        :return: True if logged, False otherwise.
+        """
+        logger = self.task.get_logger()
+        logger.report_single_value(name=tag, value=value)
+        return True
+
+    def log_scalars(
+        self,
+        tag: str,
+        values: Dict[str, float],
+        step: Optional[int] = None,
+        wall_time: Optional[float] = None,
+        level: Optional[int] = None,
+    ) -> bool:
+        """
+        :param tag: identifying tag to log the values with
+        :param values: values to save
+        :param step: global step for when the values were taken
+        :param wall_time: global wall time for when the values were taken,
+            defaults to time.time()
+        :param kwargs: additional logging arguments to support Python and custom loggers
+        :return: True if logged, False otherwise.
+        """
+        for k, v in values.items():
+            self.log_scalar(
+                tag=f"{tag}.{k}",
+                value=v,
+                step=step,
+                wall_time=wall_time,
+                level=level,
+            )
+        return True
+
+
 class SparsificationGroupLogger(BaseLogger):
     """
     Modifier logger that handles outputting values to other supported systems.

tests/sparseml/pytorch/utils/test_logger.py

@@ -20,6 +20,7 @@
 import pytest
 
 from sparseml.pytorch.utils import (
+    ClearMLLogger,
     LambdaLogger,
     LoggerManager,
     PythonLogger,
@@ -45,6 +46,7 @@
             or True
         ),
         *([WANDBLogger()] if WANDBLogger.available() else []),
+        *([ClearMLLogger()] if ClearMLLogger.available() else []),
         SparsificationGroupLogger(
             lambda_func=lambda tag, value, values, step, wall_time, level: logging.info(
                 f"{tag}, {value}, {values}, {step}, {wall_time}, {level}"
@@ -79,12 +81,12 @@ def test_log_scalar(self, logger):
 
     def test_log_scalars(self, logger):
         logger.log_scalars("test-scalars-tag", {"scalar1": 0.0, "scalar2": 1.0})
-        logger.log_scalars("test-scalars-tag", {"scalar1": 0.0, "scalar2": 1.0}, 1)
+        logger.log_scalars("test-scalars-tag2", {"scalar1": 0.0, "scalar2": 1.0}, 1)
         logger.log_scalars(
-            "test-scalars-tag", {"scalar1": 0.0, "scalar2": 1.0}, 2, time.time() - 1
+            "test-scalars-tag3", {"scalar1": 0.0, "scalar2": 1.0}, 2, time.time() - 1
         )
         logger.log_scalars(
-            "test-scalars-tag",
+            "test-scalars-tag4",
             {"scalar1": 0.0, "scalar2": 1.0},
             2,
             time.time() - 1,