removed all profiling (#364)

Co-authored-by: Fritjof Bengtsson <[email protected]>

baler/baler.py

@@ -20,7 +20,6 @@
 
 from .modules import helper
 import gzip
-from .modules.profiling import energy_profiling
 from .modules.profiling import pytorch_profile
 
 
@@ -54,23 +53,14 @@ def main():
         workspace_name,
         project_name,
         verbose,
-        pytorch_profile,
-        energy_profile,
     ) = helper.get_arguments()
     project_path = os.path.join("workspaces", workspace_name, project_name)
     output_path = os.path.join(project_path, "output")
 
     if mode == "newProject":
         helper.create_new_project(workspace_name, project_name, verbose)
     elif mode == "train":
-        check_enabled_profilers(
-            perform_training,
-            pytorch_profile,
-            energy_profile,
-            output_path,
-            config,
-            verbose,
-        )
+        perform_training(output_path=output_path, config=config, verbose=verbose)
     elif mode == "diagnose":
         perform_diagnostics(output_path, verbose)
     elif mode == "compress":
@@ -91,32 +81,6 @@ def main():
         )
 
 
-def check_enabled_profilers(
-    f, pytorchProfile=False, energyProfile=False, *args, **kwargs
-):
-    """
-    Conditionally apply profiling based on the given boolean flags.
-
-    Args:
-        f (callable): The function to be potentially profiled.
-        pytorchProfile (bool): Whether to apply PyTorch profiling.
-        energyProfile (bool): Whether to apply energy profiling.
-
-    Returns:
-        result: The result of the function `f` execution.
-    """
-    if pytorchProfile and not energyProfile:
-        return pytorch_profile(f, *args, **kwargs)
-    elif energyProfile and not pytorchProfile:
-        return energy_profiling(f, "baler_training", 1, *args, **kwargs)
-    elif pytorchProfile and energyProfile:
-        return pytorch_profile(
-            energy_profiling, f, "baler_training", 1, *args, **kwargs
-        )
-    else:
-        return f(*args, **kwargs)
-
-
 def perform_training(output_path, config, verbose: bool):
     """Main function calling the training functions, ran when --mode=train is selected.
         The three functions called are: `helper.process`, `helper.mode_init` and `helper.training`.

baler/modules/helper.py

@@ -73,12 +73,6 @@ def get_arguments():
         "  2. If workspace exists but project does not, create project in workspace.\n"
         "  3. If workspace does not exist, create workspace directory and project.",
     )
-    parser.add_argument(
-        "--pytorchProfile", action="store_true", help="Enable PyTorch profiling"
-    )
-    parser.add_argument(
-        "--energyProfile", action="store_true", help="Enable Energy profiling"
-    )
     parser.add_argument(
         "--verbose", dest="verbose", action="store_true", help="Verbose mode"
     )
@@ -104,8 +98,6 @@ def get_arguments():
         workspace_name,
         project_name,
         args.verbose,
-        args.pytorchProfile,
-        args.energyProfile,
     )
 
 

baler/modules/profiling.py

@@ -4,7 +4,6 @@
 from pstats import SortKey
 import torch
 from torch.profiler import profile, record_function, ProfilerActivity
-import codecarbon
 
 
 def pytorch_profile(f, *args, **kwargs):
@@ -54,39 +53,6 @@ def pytorch_profile(f, *args, **kwargs):
     return result
 
 
-def energy_profiling(f, project_name, measure_power_secs, *args, **kwargs):
-    """
-    Energy Profiling measures the amount of electricity that
-    was consumed by the given function f and the amount of CO2 emission.
-    It utilizes the codecarbon package for tracking this information.
-
-    Args:
-        f (callable): The function to be profiled.
-        project_name (str): The name of the project.
-        measure_power_secs (int): The number of seconds to measure power.
-
-    Returns:
-        result: The result of the function `f` execution.
-    """
-
-    tracker = codecarbon.EmissionsTracker(
-        project_name=project_name, measure_power_secs=measure_power_secs
-    )
-    tracker.start_task(f"{f.__name__}")
-
-    # Execute the function and get its result
-    result = f(*args, **kwargs)
-
-    emissions = tracker.stop_task()
-    print("CO2 emission [kg]: ", emissions.emissions)
-    print("CO2 emission rate [kg/h]: ", 3600 * emissions.emissions_rate)
-    print("CPU energy consumed [kWh]: ", emissions.cpu_energy)
-    print("GPU energy consumed [kWh]: ", emissions.gpu_energy)
-    print("RAM energy consumed [kWh]: ", emissions.ram_energy)
-
-    return result
-
-
 def c_profile(func, *args, **kwargs):
     """
     Profile the function func with cProfile.