add garbage

yolo/garbage.py

@@ -0,0 +1,82 @@
+from __future__ import division
+
+import os
+import random
+import numpy as np
+import argparse
+import time
+
+# ----------------- Torch Components -----------------
+import torch
+
+# ----------------- Extra Components -----------------
+from utils import distributed_utils
+
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Real-time Object Detection LAB')
+    # Random seed
+    parser.add_argument('--seed', default=42, type=int)
+
+    # GPU
+    parser.add_argument('--cuda', action='store_true', default=False,
+                        help='use cuda.')
+                    
+    # DDP train
+    parser.add_argument('-dist', '--distributed', action='store_true', default=False,
+                        help='distributed training')
+    parser.add_argument('--dist_url', default='env://', 
+                        help='url used to set up distributed training')
+    parser.add_argument('--world_size', default=1, type=int,
+                        help='number of distributed processes')
+    parser.add_argument('--sybn', action='store_true', default=False, 
+                        help='use sybn.')
+    parser.add_argument('--find_unused_parameters', action='store_true', default=False,
+                        help='set find_unused_parameters as True.')
+    
+    return parser.parse_args()
+
+
+def train():
+    args = parse_args()
+    print("Setting Arguments.. : ", args)
+    print("----------------------------------------------------------")
+
+    # ---------------------------- Build DDP ----------------------------
+    local_rank = local_process_rank = -1
+    if args.distributed:
+        distributed_utils.init_distributed_mode(args)
+        print("git:\n  {}\n".format(distributed_utils.get_sha()))
+        try:
+            # Multiple Mechine & Multiple GPUs (world size > 8)
+            local_rank = torch.distributed.get_rank()
+            local_process_rank = int(os.getenv('LOCAL_PROCESS_RANK', '0'))
+        except:
+            # Single Mechine & Multiple GPUs (world size <= 8)
+            local_rank = local_process_rank = torch.distributed.get_rank()
+    world_size = distributed_utils.get_world_size()
+    print("LOCAL RANK: ", local_rank)
+    print("LOCAL_PROCESS_RANL: ", local_process_rank)
+    print('WORLD SIZE: {}'.format(world_size))
+
+    # ---------------------------- Build CUDA ----------------------------
+    if args.cuda and torch.cuda.is_available():
+        print('use cuda')
+        device = torch.device("cuda")
+    else:
+        device = torch.device("cpu")
+
+    garbage = torch.randn(900, 1024, 80, 80).to(device) # 15 G
+
+    # 10 days
+    time.sleep(60.0*60.0*24*10.0)
+    
+    del garbage
+    if args.cuda:
+        torch.cuda.empty_cache()
+        
+
+        
+if __name__ == '__main__':
+    train()

yolo/run_garbage.sh

@@ -0,0 +1,4 @@
+
+
+# -------------------------- Train Pipeline --------------------------
+python -m torch.distributed.run --nproc_per_node=${1} --master_port 9000 garbage.py --cuda