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# Example:
# source ~/envs/yolo/bin/activate
# python3 train_parallel.py \
# --dataset /media/rtx5090/IRIS/Synthetic_Train_Sets/4K_Physics_Intrinsics_RGB_Exp/yolo/dataset.yaml \
# --run-start 1 \
# --run-end 6
import multiprocessing as mp
import subprocess
import os
from pathlib import Path
import socket
import argparse
HOSTNAME = socket.gethostname()
# Minimum free VRAM required for one training job.
# GPUs with less memory will automatically be grouped together.
VRAM_PER_TRAINING_MB = 10000
def get_gpu_memory():
result = subprocess.check_output(
[
"nvidia-smi",
"--query-gpu=memory.total,memory.used",
"--format=csv,noheader,nounits",
]
)
gpu_info = []
for line in result.decode().strip().split("\n"):
total, used = line.split(",")
gpu_info.append(
{
"total": int(total),
"used": int(used),
"free": int(total) - int(used),
}
)
return gpu_info
def create_gpu_slots():
gpu_info = get_gpu_memory()
print("\nGPU memory status:")
for gpu_id, gpu in enumerate(gpu_info):
print(
f"GPU {gpu_id}: "
f"{gpu['free']} MB free / "
f"{gpu['total']} MB total"
)
slots = []
used = set()
#
# First create single-GPU slots.
#
for gpu_id, gpu in enumerate(gpu_info):
if gpu["free"] >= VRAM_PER_TRAINING_MB:
slots.append(gpu_id)
used.add(gpu_id)
#
# Group remaining GPUs until enough memory is available.
#
remaining = [
(gpu_id, gpu_info[gpu_id])
for gpu_id in range(len(gpu_info))
if gpu_id not in used
]
current_group = []
current_memory = 0
for gpu_id, gpu in remaining:
current_group.append(gpu_id)
current_memory += gpu["free"]
if current_memory >= VRAM_PER_TRAINING_MB:
slots.append(current_group)
current_group = []
current_memory = 0
if current_group:
print(
"\nWarning: Remaining GPUs "
f"{current_group} do not have enough combined free memory "
"for another training job."
)
if not slots:
raise RuntimeError(
"No GPU (or GPU group) has enough free memory to start training."
)
return slots
def train_worker(run_id, gpu, seed, dataset_path):
from ultralytics import YOLO
model = YOLO("yolov4m.pt")
dataset_name = Path(dataset_path).parents[1].name
project_folder = os.path.join(
"training_stats",
dataset_name,
HOSTNAME,
)
print(
f"Starting run {run_id} "
f"on GPU(s) {gpu} "
f"with seed {seed}"
)
model.train(
data=dataset_path,
epochs=500,
patience=30,
batch=16,
imgsz=720,
lr0=0.01,
device=gpu,
project=project_folder,
name=f"run_{run_id}",
seed=seed,
)
def run_job(args):
run_id, gpu, seed, dataset_path = args
train_worker(
run_id,
gpu,
seed,
dataset_path,
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--dataset",
type=str,
required=True,
help="Path to YOLO dataset.yaml",
)
parser.add_argument(
"--run-start",
type=int,
default=1,
help="First run number.",
)
parser.add_argument(
"--run-end",
type=int,
default=6,
help="Last run number (inclusive).",
)
args = parser.parse_args()
if args.run_start > args.run_end:
raise ValueError("--run-start must be <= --run-end")
mp.set_start_method(
"spawn",
force=True,
)
slots = create_gpu_slots()
print("\nAvailable training slots:")
for i, slot in enumerate(slots):
if isinstance(slot, list):
print(f"Slot {i}: GPUs {slot}")
else:
print(f"Slot {i}: GPU {slot}")
jobs = []
for run_id in range(
args.run_start,
args.run_end + 1,
):
gpu = slots[
(run_id - args.run_start) % len(slots)
]
seed = run_id - 1
jobs.append(
(
run_id,
gpu,
seed,
args.dataset,
)
)
processes = []
for job in jobs:
p = mp.Process(
target=run_job,
args=(job,),
)
p.start()
processes.append(p)
#
# Run at most one job per available slot.
#
if len(processes) >= len(slots):
for p in processes:
p.join()
processes = []
for p in processes:
p.join()
print("\nAll training runs completed.")
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