add logging of energy consumption of entire training, add package for…

… this to `pyproject.toml`

lstm_vision/train.py

@@ -11,9 +11,10 @@
 from torch.cuda.amp import GradScaler
 from torch.nn.utils import clip_grad_norm_
 from torch.utils.data import DataLoader
+from zeus.monitor import ZeusMonitor
 
 from utils import (
-    end_timer_and_print,
+    log_training_stats,
     print__batch_info,
     save_checkpoint,
     start_timer,
@@ -77,12 +78,22 @@ def train_and_validate(
         reduction="mean", label_smoothing=label_smoothing
     )
 
+    # auxiliary variables
     start_time = start_timer(device=rank)
     train_losses, val_losses, train_accs, val_accs = [], [], [], []
     min_val_loss = float("inf")
 
+    # AMP
     scaler = GradScaler(enabled=use_amp)
 
+    # measure energy consumption (rank 0 already measures energy consumption
+    # of all GPUs)
+    if rank in [0, torch.device("cuda:0"), torch.device("cuda")]:
+        monitor = ZeusMonitor(
+            gpu_indices=[i for i in range(torch.cuda.device_count())]
+        )
+        monitor.begin_window("training")
+
     for epoch in range(num_epochs):
         t0 = start_timer(device=rank)
 
@@ -172,12 +183,17 @@ def train_and_validate(
                 "%\n"
             )
 
+    # stop energy consumption measurement
+    if rank in [0, torch.device("cuda:0"), torch.device("cuda")]:
+        measurement = monitor.end_window("training")
+
     # number of iterations per device
     num_iters = len(train_loader) * num_epochs
 
     if rank in [0, torch.device("cpu")]:
-        end_timer_and_print(
+        log_training_stats(
             start_time=start_time,
+            energy_consump=measurement.total_energy,
             device=rank,
             local_msg=(
                 f"Training {num_epochs} epochs ({num_iters} iterations)"

lstm_vision/utils.py

@@ -380,15 +380,19 @@ def start_timer(device: torch.device | int) -> float:
     return perf_counter()
 
 
-def end_timer_and_print(
-    start_time: float, device: torch.device | int, local_msg: str = ""
+def log_training_stats(
+    start_time: float,
+    energy_consump: float,
+    device: torch.device | int,
+    local_msg: str = "",
 ) -> float:
     """
     End the timer and print the time it took to execute the code as well as the
     maximum memory used by tensors.
 
     Args:
         start_time: Time at which the training started.
+        energy_consump: Energy consumption of the entire training in Joules.
         device: Device on which the code was executed. Can also be an int
             representing the GPU ID.
         local_msg: Local message to print.
@@ -412,6 +416,8 @@ def end_timer_and_print(
     msg = f"{local_msg}\n\tTotal execution time = {time_diff:.3f} [sec]"
     if device.type == "cuda":
         msg += (
+            f"\n\tEnergy consumption of entire training = "
+            f"{energy_consump / 1e3:.3f} [kJ]"
             f"\n\tMax memory used by tensors = "
             f"{torch.cuda.max_memory_allocated(device=device) / 1024**2:.3f} "
             "[MB]"

pyproject.toml

@@ -19,6 +19,7 @@ dependencies = [
     "python-dotenv~=1.0",
     "matplotlib~=3.2",
     "pre-commit",
+    "zeus-ml~=0.9"
 ]
 
 [tool.isort]