diff --git a/baler/baler.py b/baler/baler.py index ec6d59c0..85782ee9 100644 --- a/baler/baler.py +++ b/baler/baler.py @@ -20,6 +20,9 @@ from .modules import helper import gzip +from .modules.profiling import energy_profiling +from .modules.profiling import pytorch_profile + __all__ = ( "perform_compression", @@ -73,6 +76,8 @@ def main(): ) +@pytorch_profile +@energy_profiling(project_name="baler_training", measure_power_secs=1) 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`. diff --git a/baler/modules/profiling.py b/baler/modules/profiling.py new file mode 100644 index 00000000..fba9e47d --- /dev/null +++ b/baler/modules/profiling.py @@ -0,0 +1,116 @@ +import io +import pstats +import cProfile +from pstats import SortKey +import torch +from torch.profiler import profile, record_function, ProfilerActivity +import codecarbon + + +def pytorch_profile(f): + """This function perform the pytorch profiling + of CPU, GPU time and memory consumed of the function f execution. + Args: + f (_type_): decorated function + """ + + def inner_function(*args, **kwargs): + """Wrapper for the function f + Returns: + _type_: _description_ + """ + if torch.cuda.is_available(): + activities = [ProfilerActivity.CPU, ProfilerActivity.CUDA] + else: + activities = [ProfilerActivity.CPU] + # Start profiler before the function will be executed + with profile( + activities=activities, + on_trace_ready=torch.profiler.tensorboard_trace_handler( + "log/baler", worker_name="worker0" + ), + schedule=torch.profiler.schedule(wait=0, warmup=0, active=1, repeat=1), + record_shapes=True, + with_stack=True, + profile_memory=True, + ) as prof: + with record_function(f"{f.__name__}"): + # Call the decorated function + val = f(*args, **kwargs) + prof.step() + prof.stop() + # Print the CPU time for each torch operation + print(prof.key_averages().table(sort_by="cpu_time_total")) + # Store the information about CPU and GPU usage + if torch.cuda.is_available(): + prof.export_stacks("profiler_stacks.json", "self_cuda_time_total") + # Store the results to the .json file + prof.export_stacks("/tmp/profiler_stacks.json", "self_cpu_time_total") + return val + + return inner_function + + +def energy_profiling(project_name, measure_power_secs): + """Energy Profiling measure the amount of electricity that + was consumed by decorated function f and amount of CO(2) emission. + It utilize the codecarbon package for tracking this information. + Args: + f (_type_): decorated function + """ + + def decorator(f): + """Wrapper for the inner function f + Args: + f (_type_): _description_ + """ + + def inner_function(*args, **kwargs): + """_summary_ + + Returns: + _type_: _description_ + """ + tracker = codecarbon.EmissionsTracker( + project_name=project_name, measure_power_secs=measure_power_secs + ) + tracker.start_task(f"{f.__name__}") + val = 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 val + + return inner_function + + return decorator + + +def c_profile(func): + """Profile the function func with cProfile + + Args: + func (_type_): _description_ + """ + + def wrapper(*args, **kwargs): + """_summary_ + + Returns: + _type_: _description_ + """ + pr = cProfile.Profile() + pr.enable() + retval = func(*args, **kwargs) + pr.disable() + s = io.StringIO() + sortby = SortKey.CUMULATIVE # 'cumulative' + ps = pstats.Stats(pr, stream=s).sort_stats(sortby) + ps.print_stats() + print(s.getvalue()) + return retval + + return wrapper diff --git a/poetry.lock b/poetry.lock index a385e535..0a557f31 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2,15 +2,29 @@ [[package]] name = "absl-py" -version = "1.4.0" +version = "2.0.0" description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py." optional = true +python-versions = ">=3.7" +files = [ + {file = "absl-py-2.0.0.tar.gz", hash = "sha256:d9690211c5fcfefcdd1a45470ac2b5c5acd45241c3af71eed96bc5441746c0d5"}, + {file = "absl_py-2.0.0-py3-none-any.whl", hash = "sha256:9a28abb62774ae4e8edbe2dd4c49ffcd45a6a848952a5eccc6a49f3f0fc1e2f3"}, +] + +[[package]] +name = "arrow" +version = "1.2.3" +description = "Better dates & times for Python" +optional = false python-versions = ">=3.6" files = [ - 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