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[24497] Optimizations and Cleaning #13

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[24497] Optimizations and Cleaning #13

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375235f
optimization
zesk1999 May 11, 2026
24ebdcb
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zesk1999 Jun 9, 2026
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20 changes: 6 additions & 14 deletions carbon_footprint_node.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
# Copyright 2023 SustainML Consortium
# Copyright 2026 SustainML Consortium
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
Expand Down Expand Up @@ -42,11 +42,9 @@ def _parse_tracker_logs(log_dir):
try:
logs = parser.parse_all_logs(log_dir=log_dir)
except Exception as e:
# print("[CT DEBUG] parse_all_logs error:", e)
return carbon_g, energy_kwh, ci_g_per_kwh

if not logs:
# print("[CT DEBUG] no logs parsed")
return carbon_g, energy_kwh, ci_g_per_kwh

# Take the last entry (the one CarbonTracker just wrote)
Expand Down Expand Up @@ -79,7 +77,6 @@ def load_any_model(model_name, hf_token=None, unsupported_models=None, **kwargs)

try:
config = transformers.AutoConfig.from_pretrained(model_name, trust_remote_code=True)
print(f"Model configuration loaded: {config}")
model_class = transformers.AutoModel._model_mapping.get(type(config), None)

if unsupported_models is not None:
Expand Down Expand Up @@ -129,9 +126,6 @@ def load_any_model(model_name, hf_token=None, unsupported_models=None, **kwargs)
f"Error initializing tokenizer for model {model_name}: {last_tok_err}"
) from last_tok_err

if tokenizer is None:
raise Exception(f"Error initializing tokenizer for model {model_name}: {e}")

input = None
try:
# Text
Expand Down Expand Up @@ -322,7 +316,6 @@ def task_callback(ml_model, user_input, hw, node_status, co2):
is_onnx = isinstance(model_path, str) and model_path.endswith(".onnx")

if is_onnx:
global GRID_CARBON_INTENSITY

# Calibrate GRID_CARBON_INTENSITY if not set
if (not os.getenv("SUSTAINML_GRID_CI")) and (not GRID_CARBON_INTENSITY or GRID_CARBON_INTENSITY <= 0):
Expand Down Expand Up @@ -391,7 +384,6 @@ def task_callback(ml_model, user_input, hw, node_status, co2):
)
proc.start()
proc.join(timeout=75)
print("[CT DEBUG] child alive?", proc.is_alive(), "exitcode=", proc.exitcode, "queue_empty=", queue.empty(), flush=True)
if proc.is_alive():
print("Child process did not finish within the timeout period. Terminating...")
proc.terminate()
Expand All @@ -415,7 +407,7 @@ def task_callback(ml_model, user_input, hw, node_status, co2):
_, ekwh, _ = _parse_tracker_logs(log_directory)
tracker_energy_kwh = ekwh
except Exception as e:
print("[CT DEBUG] could not re-parse tracker logs in task_callback:", e)
print("Could not re-parse tracker logs in task_callback:", e)

try:
latency_h = float(hw.latency())
Expand All @@ -424,7 +416,7 @@ def task_callback(ml_model, user_input, hw, node_status, co2):
# W * h = Wh → /1000 = kWh
energy_consump_hw_kwh = (power_w * latency_h) / 1000.0
except Exception as e:
print("[CT DEBUG] HW energy compute failed:", e)
print("HW energy compute failed:", e)
energy_consump_hw_kwh = 0.0

# Choose energy source
Expand All @@ -445,11 +437,11 @@ def task_callback(ml_model, user_input, hw, node_status, co2):
carbon = carbon / inf_per_epoch
energy_consump = energy_consump / inf_per_epoch
else:
print("[CT DEBUG] inf_per_epoch <= 0, skipping per-inference scaling")
print("inf_per_epoch <= 0, skipping per-inference scaling")
else:
print("[CT DEBUG] epoch_s or latency_s <= 0, skipping per-inference scaling")
print("epoch_s or latency_s <= 0, skipping per-inference scaling")
except Exception as e:
print("[CT DEBUG] per-inference scaling failed:", e)
print("Per-inference scaling failed:", e)
else:
raise Exception("No result obtained from the tracker process; failed to obtain carbon footprint value.")

Expand Down